Sacramento Municipal Utility District Home Energy Report ...€¦ · Sacramento Municipal Utility District Home Energy Report Program Program Years 2008-2011 Report Date November

Impact & Persistence Evaluation Report

Sacramento Municipal Utility District

Home Energy Report Program

Program Years 2008-2011

Report Date November 2012

Project Lead: May Wu, MS Qa, MS Econ

[email protected]

Reviewed by Tom Osterhus, CEO, PhD, MSQa, MBA

[email protected]

With Engineering Support and Project Advisor

Pete Jacobs, PE Patricia Thompson JD

mailto:[email protected]

mailto:[email protected]

1

Table of Contents

EXECUTIVE SUMMARY .......................................................................................................................5

CHAPTER 1: PROGRAM OVERVIEW ................................................................................................... 12

BACKGROUND ..................................................................................................................................... 12

RESEARCH OBJECTIVES .......................................................................................................................... 13

CHAPTER 2: THE BILLING ANALYSIS ................................................................................................... 16

DATA PREPARATION ............................................................................................................................. 17

METHODOLOGY ................................................................................................................................... 18

CONTROL GROUPS ............................................................................................................................... 20

RESULTS OF BILLING ANALYSIS ................................................................................................................ 22

EFFECTS FOR WAVE 2 REPORT ........................................................................................................................... 23

UCLA Based Participant Selection ............................................................................................................... 24

E-Reports ..................................................................................................................................................... 28

Seasonal Burst ............................................................................................................................................. 29

PREDICTED SAVING VS. MEASURED SAVING ......................................................................................................... 30

CHANNEL EFFECTIVENESS .................................................................................................................................. 31

EFFECTS FOR WAVE 1 REPORT ........................................................................................................................... 32

Persistence .................................................................................................................................................. 33

Monthly Reports ......................................................................................................................................... 34

Quarterly Reports ....................................................................................................................................... 35

CHAPTER 3: SURVEY ANALYSIS ......................................................................................................... 36

STEP ONE: PARALLEL SURVEYS (ONLINE, MAILER AND TELEPHONE) ................................................................. 36

STEP TWO: NESTED SAMPLE AND ONSITE VISITS .......................................................................................... 39

DIFFERENCES BETWEEN THE SURVEY SAMPLE AND THE OVERALL STUDY SAMPLE ................................................. 39

SURVEY FINDINGS ................................................................................................................................ 40

SURVEY ITEM RANKINGS ................................................................................................................................... 40

SELF-REPORTED ACTION ERROR RATE ................................................................................................................. 43

FIXED EFFECT MODEL RESULTS FROM THE SURVEY SAMPLE ............................................................................ 44

CHAPTER 4: ENGINEERING ANALYSES ............................................................................................... 45

SIMULATION MODELING ........................................................................................................................ 45

2

LIGHTING AND APPLIANCES .................................................................................................................... 46

DOMESTIC HOT WATER ......................................................................................................................... 48

ENGINEERING ANALYSIS RESULTS ............................................................................................................. 49

CHAPTER 5: IMPACT OF HERS ON OTHER SMUD PROGRAMS ............................................................. 54

THE SYNERGY EFFECT OF JOINT PROGRAM PARTICIPATION ON ENERGY SAVINGS ................................................. 55

REBATE/FINANCE PROGRAM PARTICIPATION RATES ..................................................................................... 56

CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS ..................................................................... 59

3

Table of Tables Table Ex- 1: Summary of Saving and Description of Research Sub Groups ................................................ 7

Table 1- 1: Program Design from SMUD RFP .............................................................................................. 13

Table 2- 1: Sub Group Sample Size in Billing Analysis after Data Preparation ........................................ 18

Table 2- 2: Independent variables included in model ................................................................................. 19

Table 2- 3: Pre-Program Differences between Wave 1 Treatment and Control Group ............................. 21

Table 2- 4: Pre-Program Difference between E-reports and Paper Reports ............................................. 22

Table 2- 5: Pre-Program Difference between Wave 2 Treatment and Control Group .............................. 22

Table 2- 6: Predicted Saving Effect of Wave 2 ........................................................................................... 24

Table 2- 7: Overall Saving Estimate of Wave 2 .......................................................................................... 24

Table 2- 9: Saving Estimate of UCLA Based Participant Selection ........................................................... 26

Table 2- 10: Saving Estimate of SMUD Segmentation ............................................................................... 27

Table 2- 11: Saving Estimate of High Use .................................................................................................. 28

Table 2- 12: Saving Estimate of E-Reports ................................................................................................. 29

Table 2- 13: Saving Estimate of Seasonal Burst ......................................................................................... 30

Table 2- 14: Effect Size of Email vs. Paper ................................................................................................ 32

Table 2- 15: Overall Saving Estimate of Wave 1 ........................................................................................ 32

Table 2- 16: Saving Estimate of Wave 1 by Year ........................................................................................ 32

Table 2- 17: Saving Estimate of Persistence .............................................................................................. 34

Table 2- 18: Saving Estimate of Monthly Reports ...................................................................................... 34

Table 2- 19: Saving Estimate of Quarterly Reports .................................................................................... 35

Table 3- 1: Sample Size and Response Rate of Each Survey Channel ........................................................ 36

Table 3- 2: Differences between Survey Sample and Overall Study Sample ............................................... 39

Table 3- 3: Survey Item Ranking from Top to Bottom ................................................................................ 41

Table 3- 4: Saving from Survey Sample ...................................................................................................... 44

Table 4- 1: Unit Energy Consumption from RASS ..................................................................................... 47

Table 4- 2: Domestic Hot Water Consumption as a Function of Number of Occupants ............................ 48

Table 4- 3: Saving by End Use ................................................................................................................... 50

Table 4- 4: Saving by Behavioral vs. Structural Change............................................................................ 51

Table 4- 5: Saving by Individual Measure .................................................................................................. 52

Table 5- 1: Synergy Effect ........................................................................................................................... 55

Table 5- 2: Joint Program Participation .................................................................................................... 56

Table 5- 3: Joint Program Participation by Individual Program ............................................................... 58

4

Table of Graphs

Graph 2- 1: Saving Estimate by Month of UCLA Based Participant Selection .......................................... 26

Graph 2- 2: Saving Estimate by Month of SMUD Segmentation ................................................................ 27

Graph 2- 3: Saving Estimate by Month of High Use .................................................................................. 28

Graph 2- 4: Saving Estimate by Month of E-Reports ................................................................................. 29

Graph 2- 5: Saving Estimate by Month of Seasonal Burst ......................................................................... 30

Graph 2- 6: Saving Estimate by Month of Wave 1...................................................................................... 33

Graph 2- 7: Saving Estimate by Month of Persistence ............................................................................... 34

Graph 2- 8: Saving Estimate by Month of Monthly Reports ....................................................................... 35

Graph 2- 9: Saving Estimate by Month of Quarterly Reports .................................................................... 35

Graph 4- 1: Weekday Lighting / Appliance Load Shape ............................................................................ 47

Graph 4- 1: Weekday Lighting / Appliance Load Shape ............................................................................ 47

Graph 4- 2: Weekday Domestic Hot Water Load Shape ............................................................................ 49

Graph 4- 3: Saving by End Use .................................................................................................................. 50

Graph 4- 4: Saving by End Use within Lights & Appliances ..................................................................... 51

Graph 4- 5: Saving by Behavioral vs. Structural Change .......................................................................... 51

5

Executive Summary

This report presents the results of an impact evaluation study conducted by Integral Analytics

(IA) on Sacramento Municipal Utility District (SMUD)’s Home Electricity Report program. The

Home Electricity Reports provided feedback on electricity consumption in the home compared to

similar neighbors. The reports also provided tips on how each household could save electricity.

Two waves of Home Electricity Reports were evaluated for impacts. The first wave of reports

studied began in April 2008 with recipients receiving either monthly or quarterly reports. For

Wave 1 the impacts were assessed for the period of April 2008 through September 2011. The

second wave of reports started in October 2010 with impacts assessed from October 2010

through September 2011 (shortly before IA was contracted by SMUD to conduct the evaluation.)

The evaluation study included three approaches: 1) meter data analysis and billing analysis to

assess net impacts, 2) three separate surveys asking about types of customer responses to the

report as well as 3) engineering studies to verify customer actions in the field and populate

engineering models. These three approaches were not only used to “bound” possible impacts,

but were also nested within each other. For example the billing analysis (what actually

happened) was used to parameterize the impacts from the engineering models, which were

driven by survey responses and in-field verifictation. This resulted in an overall tighter estimate

on what kind of changes were driving the savings observed.

The billing analysis was conducted using a pre-test / post-test control group design. A treatment

group of approximately 55,000 SMUD households received Home Electricity Reports. A control

6

group of 84,555 households did not receive the Home Electricity Reports. It is important to note

that both of the original treatment and control groups were selected by the implementation

contractor. Small but detectable differences between the treatment and control groups for both

waves were documented. IA used a fixed effect panel model in the billing analysis to estimate

the overall savings in kilowatt hours (kWh) attributable to the Home Electricity Reports.

The billing analysis (described in greater detail in Chapter 2) also examined net kWh savings of

eight research subgroups. Wave 1 was disaggregated into three subgroups: monthly report

recipients, quarterly report recipients as well as a third group that stopped receiving reports, to be

able to assess the persistence of energy savings. Wave 2 contained five sub groups: UCLA

Selection, SMUD Segmentation, High Use, E-reports and Seasonal burst. Because there was a

significant time lapse between Wave 1 and Wave 2 the team conducted separate analyses on

each Wave. The team found that the Home Electricity Reports had a net impact of 2.2 percent

savings per month per household in Wave 1. This equates to an annual mean energy savings of

approximately 250 kWh per household. The reports had a net impact of 1.6 percent per month

per household in Wave 2, equivalent to an annual mean savings of approximately 216 kWh.

Note that these results controlled for SMUD program participation. As a result there is no

“double counting.” However the results do include a synergistic effect documented by the

research team described in Chapter 5. The team found that homes that make a structural change

with SMUD efficiency rebate dollars that also receive the HER go on to save more than homes

that merely participate in a rebate program.1 Table Ex-1 indicates each of the subgroup savings

in greater detail.

1 Given that relatively small number of HER program participants also participated in some level

of SMUD rebate program since report inception (approximately 3% per year) it is difficult to

accurately estimate what would have happened in the absence of SMUDs rebate programs.

7

Table Ex- 1: Summary of Saving and Description of Research Sub Groups

Report Waves and Subgroups % Savings* Annual Savings

(kWh)***

Monthly Average Pre-Program Use of

Subgroup( kWh)****

Treatment group size

Study Start Date

Study End Date

Wave 1 -2.2% -249.7

947 33,968 Apr. 2008 Sep. 2011

Wave 1 2008 -1.8% -207.4

Wave 1 2009 -2.4% -275.9

Wave 1 2010 -2.4% -270.5

Wave 1 2011 -2.1% -237.8

Persistence** -1.6% -179.2 948 9,965 Apr. 2008 Sep. 2011

Monthly Report -2.4% -325.7 1137 17,018 Apr. 2008 Sep. 2011

Quarterly Report -1.4% -83.3 488 6,985 Apr. 2008 Sep. 2011

Wave 2 -1.6% -216.7 1099 20,807 Oct. 2010 Sep. 2011

UCLA Selection -2.2% -264.6 1025 3,359 Oct.2010 Sep. 2011

SMUD Segmentation -1.7% -256.4 1240 3,250 Oct.2010 Sep. 2011

High Use -2.7% -434.6 1343 3,292 Oct.2010 Sep. 2011

E-Reports -1.8% -168 772 5,930 Oct.2010 Sep. 2011

Seasonal Burst -1.2% -177.6 1203 4,976 Oct.2010 Sep. 2011

*Negative =savings

**Reports stopped July 2010. The Persistence result presented in the table reflects an annual savings amount for the first year after

report cessation. However the study team relied on usage data throughout the research period to arrive at this result.

*** For partial program year, the annual saving is projected

**** Average of both treatment and control group members

8

Other key findings on the overall net impact of the Home Electricity Reports include:

Targeting High Use customers yielded the most savings at 36 kWh per month which is

equivalent to 2.7% or about 435 kWh annually;

The strategy of sending targeted Seasonal Burst notifications yielded 178 kWh saving

annually which is equivalent to 1.2% mean annual savings. The monthly average of about

15 kWh masks the seasonal impacts associated with this approach, which are closer to 25

kWh per month at the height of summer. The strategy also yielded 0.06 kW reductions

on summer peak days. Given that this strategy only involved 4 mailers it appears to be an

effective strategy in terms of overall cost effectiveness.

Email reports as a delivery channel and targeting strategy did not appear to be as

effective as paper reports. Note because E-report recipients were not selected in a same

manner as paper report recipients, it was not straightforward to compare their net energy

saving impact. The team was provided a control group that was representative of the

general population (rather than households with comparable usage and characteristics to

those receiving the E-reports) estimate the effect size of the two channels. Using this

method E-reports were estimated to be only one third as effective as paper reports. A

more comprehensive analysis is required to determine cost effectiveness as email reports

may be a less costly and a potentially preferred customer channel.2

The team used a polynomial function to assess the rate of savings decay. The

extrapolation indicates that savings does appear to decay following report cessation. The

polynomial function would appear to decay to zero approximately 2 years after the

reports were halted. This is only the projection based on current data; longer observation

is needed to assess persistence given that the research also showed some of the savings

appear to be structural in nature.

In addition to the billing analysis IA also worked to uncover the actions undertaken by report

recipients, to better discern the source of the energy savings. The extent to which, say, a 2%

energy savings is caused by physical changes or equipment savings, versus behavioral actions,

may provide insights into the potential long term persistence of the energy savings beyond one

year. Three energy surveys (online, mailer and phone) were designed to identify the energy use

changes reported by Home Electricity Report recipients that were responsible for producing the

energy savings observed in the billing analysis. In addition, IA also conducted on-site

examinations to collect building engineering characteristics (described in Chapter 4) to capture

and verify self-reported actions. Not surprisingly we found that the energy report users more

commonly undertook behavioral actions as compared to energy efficiency equipment (structural)

changes.

2 More analysis is required to assess the magnitude of this potential bias in the provided control

groups for this segment.

9

The most common kinds of behavioral changes cited by survey respondents included the

following:

Set your thermostat wisely for comfort and savings

Get the lint out of dryers

Clean or replace air filters

Light only needed areas

Only run washer with full loads

Let the sun in for warmth

Turn off your computer at night

The most common kinds of structural changes cited by survey respondents included the

following:

Switch to compact fluorescent bulbs

Upgrade to a more energy efficient refrigerator

Upgrade to a more efficient clothes washer

Nearly one quarter of survey respondents reported taking behavioral action as opposed to

roughly 15% of respondents who reported under taking structural changes. From an energy

savings perspective those structural changes accounted for approximately 40% of the savings

while behavior (including behaviors that interact with equipment) account for 60% of the savings

based on engineering analysis.

The final key question addressed by the evaluation was whether dual participation in other

SMUD energy conservation programs contributed to some of the observed kWh savings instead

of being directly attributable to the Home Electricity Reports. Here we found that exposure to the

Home Electricity Reports appears to have had a modest or weak positive influence on

participation in these SMUD rebate and financing programs. Prior to report exposure, program

take rate in the control group was slightly higher than in the treatment group (4.4% vs. 4.2%).

After report exposure take rate of treatment group became slightly higher than control group

(3.1% vs. 2.9%).

The report program is modestly effective at increasing the awareness and availability of these

SMUD offerings, to some extent. However, it is not known whether the use of this type of report

program as a promotional vehicle for SMUD’s efficiency portfolio is a more cost effective

option than direct program specific marketing promotional dollars. The top programs whose

response rate increased after exposure to the Home Electricity Reports are listed below.

10

Residential Refrigerator Recycling Program3

Heat Pump Split System

Detailed conclusions and recommendations are included in Chapter 6. A synopsis of high level

conclusions and recommendations for potential improvements include:

If the E-report is considered a major strategy for the mass market, the team recommends

further testing this strategy by selecting a representative treatment sample and control

sample, where both are online account users (i.e. Your Account users) with no significant

usage constraints imposed by the implementer in the sample selection process.

It is important to realize the population for the E-report may naturally be biased

compared to the population for the paper report. If the end goal is to determine net cost

effectiveness per report type, or per channel and/or market segment, it may be beneficial

to conduct a brief cost benefit analysis of the estimated savings from each subgroup

versus the costs to implement for that group.

Given that the eight sub groups were covered several distinct customer segments their

saving impacts should be interpreted with caution. It is not straightforward to compare

between group saving impacts or to extrapolate saving impacts out of the current research

sub groups to the broader population. While Wave 2 groups all had at least average to

high use the research team believes that this may not be the only driver of savings. For

example the online users were selected for sharing characteristics with the customers in

the pilot that saved more than average but they retain the difference of having gone

online with SMUD. They also have lower kWh usage than the other Wave 2 households.

Thus they are also demonstrably different. Unless these forward strategies would always

be applied to the same segments, IA recommends leveraging more sub groups which are

representative of the SMUD population, to accurately determine program impacts on the

broader market.

Consider the use of incentives and follow-up procedures for increasing the survey

response rates. Survey non-response biases may or may not exist, and may or may not be

consequential. On the other hand, the billing analysis methodology is not subject to these

same non-response biases. As such, pursuit of non-response quantification may not be

worth the additional expense, but we note the potential bias here for consideration.

The team also recommends two items for future study.

3The purpose of the Residential Refrigerator Recycling Program is to permanently remove old refrigerators

and/or freezers from customer’s homes and transport them to a recycling center for demolition and

recycling of components in an environmentally responsible manner.

11

Given that all program participants have had access to the interactive online tools

associated with the program since September, 2010 it would be beneficial to evaluate

whether the reports led to an increased online use, and if online tool use is related to

savings. Web log data would be required to quantify this lift to assess scaling decisions if

any.

Energy savings seems to increase in January, suggesting that electric heating segments

might benefit from more targeted messaging in December, or colder time periods. If the

source of increase is not from electric heating use, then perhaps targeted focus groups

might elicit the root cause, and value to be gained, from more specific January targeting.4

4 IA has agreed to undertake this additional analysis and will provide SMUD an update on the

components of winter savings in an update memo.

12

Chapter 1: Program Overview

Background SMUD’s Home Electricity Reports are designed to provide motivation, information and

education to residential customers about their electricity usage so that they can effectively lower

their consumption and their monthly SMUD bill. The Home Electricity Reports are distributed to

targeted customers free of charge, monthly or quarterly, and include:

Last Month Neighbor Comparison

Twelve Month Neighbor Comparison

Personal Comparison

Personalized Action Steps

Behavioral science theory has inspired these types of Home Electricity Reports, which use

normative messaging to motivate residential customers to conserve energy through behavioral

changes and to make energy efficiency upgrades. SMUD operated this program in a pilot phase

from April 2008 through March 2009. SMUD staff conducted a preliminary billing analysis of

the pilot program and found a 1.75% program savings for the first six-months of program

operation in October 2008. A more comprehensive evaluation was performed in 2009 by a third

party that resulted in overall program savings estimates of 1.9% for the full test group, and 1.4%

savings for customers who did not participate in any other SMUD rebate and financing

programs.

In July, 2010, reports were halted to a portion of the pilot group in order to test the persistence of

energy savings that may endure after recipients stop receiving reports. In September, 2010, the

program was expanded to an additional 25,000 recipients who were targeted because they shared

characteristics with the highest energy savers from the pilot group. Three groups of 5,000

customers were selected using different targeting strategies, and are receiving monthly paper

reports in the mail. Of the remaining 10,000, which are a mix from all three targeting strategies,

5,000 received a “seasonal burst” of four monthly paper reports from May through August to

gauge the effectiveness of this approach in heightening awareness of energy use during SMUD’s

peak season. The other 5,000 received electronic reports: an email is sent notifying them of the

availability of their online report, with a click-through link to the log-in page to access their

report and other available web services.

Table1-1 shows the breakdown of current and past program participants and the two control

groups based on the original RFP:

13

Table 1- 1: Program Design from SMUD RFP

Test Sample Description Date Reports

begun

Date reports

stopped

Customers

at

beginning

of test

Customers

on 9/1/11

(forecast)

Pilot Group for whom reports sent for 27

months, then halted for persistence test April, 2008 July, 2010 7000 6350

Pilot Group for whom reports sent

continuously April, 2008

Continuing as

of Sept, 2012 18000 16300

Control for original Pilot control group April, 2008 N/A 49000 43500

Phase II Monthly, Target Group A Oct, 2010 Sept, 2011 5000 4250

Phase II Monthly, Target Group B Oct, 2010 Sept, 2011 5000 4250

Phase II Monthly, Target Group C Oct, 2010 Sept, 2011 5000 4250

Phase II Seasonal Burst (mix of targets A-

C) Oct, 2010 Sept, 2011 5000 4250

Phase II Electronic Reports (mix of

targets A-C) Oct, 2010 Sept, 2011 5000 4250

Control for Phase II targets Oct, 2010 N/A 25000 18500

Since September, 2010, all program participants have had access to the following interactive

online tools associated with the program:

A list of energy saving tips customized based on data SMUD has about the household

More detailed information on energy savings tips than what is available in paper reports

Electronically reproduced graphs of neighbor energy use comparisons customizable by time

period

The ability to add more information about the household

The ability to set an energy savings goal and track their progress towards reaching the goal

The ability to commit to making specific energy saving improvements as part of a plan for

saving energy.

Research Objectives SMUD’s overall research objective for the Residential Home Electricity Report Program

evaluation is to measure and verify the kWh and kW savings attributable to this program for the

period April, 2009 through September 2011. All measurable savings from the individual test

groups will be compared to the savings in the pilot year, and combined with survey results to

construct a picture of how savings attributable to the Home Electricity Reports change over time,

including what happens when customers stop receiving these reports.

14

The following research tasks were performed for the seven treatment groups receiving SMUDs’

Home Electricity Reports:

1. Update the evaluation results for the original pilot group (initial sample size 35,000):

1.1. Estimate the change in overall energy consumption attributable to the program after 39

months for continuous recipients of the reports as compared to a control group (gross and net

annual kWh savings and kWh savings by month and year).

1.2. Estimate the change in overall energy consumption attributable to the program for recipients

who had reports halted after 27 months, and the change over the subsequent 12-month

period (gross and net annual kWh savings and kWh savings by month and year).

2. For program participants added in September, 2010:

2.1. Estimate the change in overall energy consumption attributable to the program after 12

months as compared to a control group (gross and net annual kWh savings and kWh savings

by month and year).

2.2. Compare the change in energy use for each of the three different target groups:

UCLA target selection

SMUD market segmentation

High energy users

2.3. Compare the change in energy use of recipients of paper reports vs. recipients of electronic

reports for participants selected in the same manner.

2.4. Compare the change in energy use and estimated peak demand impact of the “seasonal

burst” strategy versus continuous monthly reports for the period June-September, 2011.

3. For all program participants:

3.1. Estimate average electrical demand savings in kW for each year (Peak period: 4-7 pm, Days:

Three consecutive days of a heat storm preceding and including the day in which the annual

peak is set) based on estimates of how much of the kWh savings came from specific end

uses and measures and application of load shapes to be provided by SMUD.

3.2. Estimate the proportion of the total kWh savings attributable to specific end uses and

common energy efficiency measures where possible.

3.3. Estimate amount of savings resulting from structural changes (i.e. equipment replacement)

vs. behavioral changes (i.e. how the equipment was operated).

3.4. Determine the percentage of savings attributable to actions taken with support from other

SMUD programs.

3.5. Determine increase in participation of targeted programs and services.

3.6. Document program assumptions and new revised assumptions due to evaluation results.

15

3.7. Provide program improvement suggestions.

4. For the two control groups:

4.1. Review the initial control group of approximately 49,000 and report on its’ ability to be used

as a non-biased control group.

4.2. Review the second control group of approximately 25,000 and report on the methodology of

its selection and its ability to be used as a non-biased control group.

4.3. Recommendations how to better improve, where needed, the selection of various control

groups.

Integral Analytics, Inc.’s, general approach was to use billing analysis to answer research

questions 1 and 2. We then approached most questions within research questions 3 through a

series of surveys collected from multiple channels. A first round of surveys including online,

mailer, telephone surveys were conducted to collect data about actions taken because of the

reports, followed by on-site visits to verify self-reported data and calibrate the survey results.

These data were analyzed within a DOE II engineering model framework. Some items within

question 3 were answered based on both billing analysis and engineering models, such as

Question 3.4. Finally, we addressed research Question 4 through additional billing analyses

involving an examination of usage patterns, home age and home sizes.

16

Chapter 2: The Billing Analysis

Research Objectives Addressed in This Chapter:

1. Update the evaluation results for the original pilot group (initial sample size 35,000):

1.3.Estimate the change in overall energy consumption attributable to the program after 39

months for continuous recipients of the reports as compared to a control group (gross

and net annual kWh savings and kWh savings by month and year).

1.4.Estimate the change in overall energy consumption attributable to the program for

recipients who had reports halted after 27 months, and the change over the subsequent

12-month period (gross and net annual kWh savings and kWh savings by month and

year).

2. For program participants added in September, 2010:

2.1.Estimate the change in overall energy consumption attributable to the program after 12

months as compared to a control group (gross and net annual kWh savings and kWh

savings by month and year).

2.2.Compare the change in energy use for each of the three different target groups:

UCLA target selection

SMUD market segmentation

High energy users

2.3.Compare the change in energy use of recipients of paper reports vs. recipients of

electronic reports for participants selected in the same manner.

2.4.Compare the change in energy use and estimated peak demand impact of the “seasonal

burst” strategy versus continuous monthly reports for the period June-September, 2011.

4. For the two control groups:

4.1. Review the initial control group of approximately 49,000 and report on its’ ability to be

used as a non-bias control group.

4.2. Review the second control group of approximately 25,000 and report on the

methodology of its selection and its ability to be used as a non-bias control group.

4.3. Recommendations how to better improve where needed the selection of various control

groups.

For this impact evaluation, data are available both across households (i.e., cross-sectional) and

over time (i.e., time-series). With this type of data, known as “panel” data, it becomes possible to

control, simultaneously, for differences across households as well as differences across periods

in time through the use of a “fixed-effects” panel model specification. The fixed-effect

specification refers to a model specification where differences across homes do not vary over the

estimation period (such as square footage, heating system, etc.). By using customer-specific

17

intercept terms that capture the net change in consumption due to the program, we control for

both those factors that do not change over time, in addition to those factors that do change with

time (e.g., the weather). A more complete description of the estimation process can be found in

Appendix U.

Data Preparation The data used in this impact evaluation came from two primary sources:

Monthly billing data from Apr. 2007 to October, 2011, obtained directly from SMUD

Historical weather conditions (HDD, CDD, and temperature) from obtained directly from

SMUD

Other SMUD rebate and financing program participant data obtained directly from

SMUD

Home Electricity Report recipients, research group membership, program launch date

specific to each customer (Wave 1 and Wave 25) from SMUD and OPower.

In order to develop the dataset used for the statistical analysis, the evaluation team conducted the

following data processing steps:

Removed overlap from SMUD Summer Solutions participants6

Removed records if monthly kWh reading is less than 50 kWh

Determined the usage on a calendar month basis for each customer based upon their read

cycle.

Linked this usage data with the appropriate weather data.

Linked the usage and weather data with the 1) customer-specific program start date,

2)whether or not and when the customer participated in any other SMUD rebate and

financing programs, and 3) the customer’s opt-out date (if appropriate).

There are a total of eight sub research groups, three of which were implemented in Wave 1; five

of which were implemented in Wave 2. The sub group membership distribution is presented in

Table 2-1:

5 SMUD operated this program in a pilot phase from April 2008 through March 2009 as Wave 1.

In September, 2010, the program was expanded to an additional 25,000 recipients as Wave 2. 6 There are only 156 overlapped customers. The reason to remove them is to ensure accurate impact (both kWh and

kW) estimates is not double counted for these summer pilots

18

Table 2- 1: Sub Group Sample Size in Billing Analysis after Data Preparation

Sub_Group_Code

Wave Sub_group_Desc Report Start

Recipient_Status # of Members

1 1 Persistence Test Apr-08 CONTROL 14,691

1 1 Persistence Test Apr-08 RECIPIENT 9,965

2 1 Monthly Report Apr-08 CONTROL 24,663

2 1 Monthly Report Apr-08 RECIPIENT 17,018

3 1 Quarterly Report Apr-08 CONTROL 10,191

3 1 Quarterly Report Apr-08 RECIPIENT 6,985

4 2 UCLA Based Participant

Selection Oct-10 CONTROL 4,981

4 2 UCLA Based Participant

Selection Oct-10 RECIPIENT 3,359

5 2 SMUD Segmentation Oct-10 CONTROL 4,949

5 2 SMUD Segmentation Oct-10 RECIPIENT 3,250

6 2 High User Selection Oct-10 CONTROL 4,951

6 2 High User Selection Oct-10 RECIPIENT 3,292

7 2 E-Reports Oct-10 CONTROL 4,940

7 2 E-Reports Oct-10 RECIPIENT 5,930

8 2 Seasonal Burst May-11 CONTROL 14,881

8 2 Seasonal Burst May-11 RECIPIENT 4,976

Methodology The fixed effects model can be viewed as a type of differencing model in which all

characteristics of the home, which (1) are independent of time and (2) determine the level of

energy consumption, are captured within the customer-specific constant terms. In other words,

differences in customer characteristics that cause variation in the level of energy consumption,

such as building size and structure, are captured by constant terms representing each unique

household.

Algebraically, the fixed-effect panel data model is described as follows:

ititiit xy ,

where:

yit = energy consumption for home i during month t

i= constant term for site i

19

ß = vector of coefficients

xit = vector of variables that represent factors causing changes in energy consumption

for home i during month t (i.e., weather and participation)

it = error term for home i during month t.

With this specification, the information necessary for estimation is those factors that vary month

to month for each customer, and which will affect energy use, which effectively are weather

conditions and program participation. Other non-measurable factors can be captured through the

use of monthly indicator variables. Loosely speaking, this approach enables the specification of

“monthly control groups” which serve as a more appropriate model specification, controlling for

unobserved factors that intervene month to month, versus simply using one average control

group for the 12 month time period.

The impact of the program was estimated by including a variable which is equal to one for all

months after the customer first received the Home Electricity Report. Thus the coefficient on

this variable is the savings associated with any general interaction with the online program.

Finally, in order to account for differences in billing days, each usage value (and the

corresponding temperature variables) was normalized by the number of days in the billing cycle

and rolled up to a calendar month level. Table 2-2 identifies the set of predictors (X variables)

that were entered in the fixed effect panel model. The coefficient estimate of variable PART

holds the estimated impact from the Home Electricity Report.

Table 2- 2: Independent variables included in model

Variable Name Variable Definition Measurement Scale

CONT_ACC Customer contract account number Categorical

M200704 – M201109 Monthly indicator of each unique month Dummy

CDD Cooling Degree Days Numeric

HDD Heating Degree Days Numeric

CDD* Post Response to CDD after report inception Numeric

HDD*Post Response to HDD after report inception Numeric

CDD*Treatment Treatment group response to CDD Numeric

HDD*Treatment Treatment group response to HDD Numeric

OTHER_DUM If participated in any other SMUD program Dummy

OTHERAFTER_DUM If participated in other SMUD program during

program years Dummy

PART Interaction of treatment and post_program period Dummy

Fixed effect panel models were developed for each unique sub group and then run on the full

sample, the results of which are reported below. The dependent variable in the analyses was the

standardized monthly kWh7 per household.

7 Note the monthly kWh was standardized on calendar month level

20

Control Groups Before performing billing analysis the control groups’ ability to be used as a non-biased control

group was assessed per the RFP. The methodology of how Opower selected its control group for

Wave 1 customers was detailed in the ADM’s 2009 evaluation report, as well as concerns and

issues with the quasi-experimental sample design8. The comparison of demographic factors and

usage levels are summarized in Table 2-3.

8 The Impact of Home Electricity Reports, ADM Associates, 2009. Selection of Treatment and Control

Groups indicates:

The Home Electricity Report pilot program was set up by SMUD and its implementation contractor – Positive

Energy – with treatment and control groups to enable a scientifically valid impact evaluation to be carried out.

Positive Energy (PE) assigned households from SMUD’s customer database to treatment and control groups using

the following methodology.

PE selected 85 census tracts from the SMUD service territory that were geo-codable and had a high density of

single-family homes with addresses that could be verified with the county assessor’s office.

Additional criteria were applied to households in these census tracts for a house to be eligible for inclusion in the

study as either a test or control site. This resulted in the identification of approximately 84,000 residential homes as

a consequence of applying the following criteria:

A household must be on one of the primary meter read cycles for that census tract.

A household must have a current, active account with SMUD.

A household must be residential and not an apartment building.

A household must have a square footage value between 250 and 99,998.

A household’s first bill date must be at last 12 months prior to the start of the pilot program.

Groups of contiguous census blocks (groups of 50-200 homes) were then randomly assigned to either the treatment

or control group. The process was to first randomly assign a census “block batch” of five contiguous census blocks

to the treatment group and then randomly assign a contiguous census “block batch” to the control group. This

process continued until approximately 35,000 residential homes had been assigned to the treatment group and

35,000 homes had been assigned to the control group. The remaining census blocks and 14,000 homes were then

also assigned to the control group. The logic for using the contiguous “block batch” method of random assignment

was PE’s “network effects” hypothesis. This untested hypothesis asserts that “energy savings could be higher if an

entire community is engaged rather than individual households.” That is, PE believed that a synergy effect would

increase energy savings because of increased communication among people in the same community who received

Home Electricity Reports.

21

Table 2- 3: Pre-Program Differences between Wave 1 Treatment and Control Group

Household Characteristic Treatment Group Pre-

Program Mean

Control Group Pre-

Program Mean T-C Difference

Annual Use (kWh) 11,221 11,365 -144

Summer Use (kWh) 3,305 3,344 -39

Home Age in Years 39 41 -2

Square Feet 1,500 - 1,749 1,500 - 1,749 0

The differences shown in Table 2-3 suggest that Wave 1 usages were slightly biased in favor of

the treatment group such that treatment group members are compared to older homes that

consumed more energy annually and peaked higher in the summer. No difference was detected

in terms of home size. The other between-group differences appear to be lacking in substantive

significance.

In 2010 the program expanded to include additional customers which were labeled as Wave 2

customers. Specifically OPower’s eligibility criteria were screened for SMUD’s exclusion

criteria9. The revised eligibility list was considered the population.

The population was randomly divided into quarters.

Highest saving targets were selected from each of three quarters and divided into

treatment and control groups. Only one targeting method was used for each quarter to

eliminate bias. The three quarters correspond to the three sub groups of UCLA Selection,

SMUD Segmentation and High Use.

From the fourth quarter, the following random selections were made in this order:

o Random control sample

o Random sample of customers to receive the generic SMUD letter which was not

implemented

o Random sample of those Your Account customers which was labeled as E-reports

Wave 2 control groups were selected based on a randomized experiment. The magnitude of

annual use differences is smaller than Wave 1. The team finds no problem with this control

group selection method except potential bias in the sub group of E-reports whose usage is

significantly lower than the other sub groups and seems to be a unique customer segment. The

9 Eligibility criteria include:

At least 12 months of usage data available at the current address

Not a member of a previous HER treatment or control group

Single family home

Excluding Non-residential, including Mobile Home Parks (RMHP)

Excluding Multi-family dwellings (based on NAICS code)

Excluding PV (based on the pv_gen code)

Excluding Customers on service less than 3 months

22

research team was given a control group to compare the E-report recipients to that was generally

more reflective of a random control sample. But the E-report recipients were online users and

therefore potentially different from the E-report recipients. Moreover the usage of the E-report

recipients appears to be lower than the paper reports. Table 2-4 summarizes pre-program usage

differences between E-reports and the other sub groups.

Table 2- 4: Pre-Program Difference between E-reports and Paper Reports

Household Characteristic E-Reports Paper Reports E - P

Annual Use (kWh) 9,899 14,537 -4638

Summer Use (kWh) 2,941 4,088 -1147

Winter Use (kWh) 2,443 3,760 -1317

If the E-report is considered a major strategy for the mass market, the team recommends further

testing this strategy by selecting a representative treatment sample and control sample, where

both are online account users (i.e. Your Account users) and softening usage constraints to

provide sufficient sample. The comparison of demographic factors and usage level is

summarized in table 2-5.

Table 2- 5: Pre-Program Difference between Wave 2 Treatment and Control Group


Program Mean

Control Group Pre-

Program Mean T-OPC Difference

Annual Use (kWh) 13,216 13,121 95

Summer Use (kWh) 3,761 3,717 44

Home Age in Years 33 34 -1

Square Feet 1,500 - 1,749 1,500 - 1,749 0

The differences shown in Table 2-5 suggest that Wave 2 control members consumed less than

treatment members on average and seasonally. They were slightly biased in favor of the control

group. No difference was detected in terms of home size. Homes in control group are 1 year

older than treatment homes on average.

Results of Billing Analysis The models estimated in the billing analysis included the following. We started with billing

analysis on Wave 2 a report given it is of more interest to SMUD staff.

Effects for Wave 2 reports

Target Selection and Predicted Saving

Measured Saving

o UCLA

23

o SMUD Segmentation

o High use

o E-Reports

o Seasonal burst

Effects for Wave 1 reports

o Monthly

o Quarterly

o Persistence

Effects for Wave 2 report

According to SMUD, in September 2010 SMUD expanded the Home Electricity Report program

to an additional 25,000 customers (Wave 2) who were targeted because they shared

characteristics with the highest energy savers from the pilot group. Three groups of 5,000

customers were selected using different targeting strategies (UCLA, SMUD Segment, High Use),

and are receiving monthly paper reports in the mail to compare the three different methods for

targeting customers who were believed likely to save more than average. The intent was to

determine which targeting method was the best and most cost effective predictor of savings.

The remaining 10,000 were a mix of all three targeting strategies: half received a “seasonal

burst” of four monthly paper reports from May through August to assess peak reduction

potential. The remaining 5,000 received electronic reports: an email was sent notifying them of

the availability of their online report, with a click-through link to the log-in page to access their

report and other available web services.

For each of the three target groups (UCLA, SMUD, High Use), a pool of customers was created

from which a test and control group were randomly selected. It should be noted that there was

substantial overlap between these customer groups, given that each segment shared

characteristics such as higher than average electricity consumption, raising the challenge of how

to allocate members to each target group without introducing selection bias. The ideal way to

avoid selection bias while maximizing the expected savings would have been to make a round-

robin alternating allocation to each target group and its corresponding control group from the

pool based on the membership criteria for that target group, thereby selecting targets with the

highest potential savings. However, this option was deemed too labor intensive. Instead, the

available population of eligible customers was randomly divided into quarters and the highest

saving targets were selected from each of three quarters and divided into treatment and control

groups.

Only one targeting method was used for each quarter in order to eliminate bias. But this resulted

in dilution of the ideal target pools: members for each target were randomly selected from among

the 40,000 best candidates rather than the 10,000 best candidates, since 75% of the best

candidates for a particular target selection method were in the other quarters and therefore

unavailable to the target. From the fourth quarter, random selections were made for a control

24

group, a target group selected from among SMUD “Your Account” enrollees that would receive

electronic versions of the home electricity reports, and a Hawthorne control group. However the

Hawthorne test was never implemented due to lack of staff resources.

The following table shows the expected electricity savings effect for each target group.

Table 2- 6: Predicted Saving Effect of Wave 2

Target Group Number

Projected Effect (% Change in

kWh/yr) Projected Effect

(kWh/yr)

Observed Effect

(kWh/yr)

SMUD Segmentation 5000 -3.6% -677 -256

UCLA Selection 5000 -5.7% -584 -265

High Usage 5000 -3.0% -179 -435

What the team observed was an overall saving from Wave 2 reports of 18 kWh per month or216

kWh annually, roughly 1.6%.

Table 2- 7: Overall Saving Estimate of Wave 2

Statistics Coefficient (kWh) Pr > |t| Percentage Saving

(%)

Monthly Saving kWh -18.06 <.0001 -1.58%

Sample Size 55,509 homes10

R-Squared 79%

The evaluation team ran a fixed effects panel model on each unique sub groups during the Wave

2 period. These sub groups are: UCLA Based Participant Selection, SMUD Segmentation, High

Use, E-Reports and Seasonal Burst.

UCLA Based Participant Selection

The UCLA selection method was developed by Mathew Kahn and Dora Costa of UCLA based

on multiple customer attributes that were shown to correlate with high savings in a multivariate

regression model that they applied to pilot participants. UCLA ran data for all of SMUD’s

residential customer accounts through the model, which assigned predicted energy savings from

the Home Electricity Reports to each household based on the degree of weighted correlation for

each variable. The accounts were than sorted by predicted savings with the intent on selecting the

highest potential savers within the constraints of the selection process. The average net savings

10

Number of homes includes both homes in treatment group and homes in control group.

25

predicted for the target was 528 kWh per year—substantially higher than ADM’s savings

estimate for the pilot group of 160 kWh/yr.

The sub group of UCLA Based Participant Selection yields 22 kWh monthly savings, which is

equivalent to 2.2% savings. Savings are also decomposed on monthly basis, which shows a

relatively stable pattern across months. The results represent a modest improvement (265 versus

250 kWh for Wave 1 annually) annual Table 2-7 summarizes model result of the UCLA Based

Participant Selection. Graph 2-1 plots the monthly savings. Both point estimates and confidence

intervals are plotted, with confidence intervals defining the boundary of statistical estimates.

26

Table 2- 8: Saving Estimate of UCLA Based Participant Selection


(%)

Estimated Monthly Saving

-22.05 <.0001 -2.2%

Sample Size 8,340 homes

R-Squared 82%

Graph 2- 1: Saving Estimate by Month of UCLA Based Participant Selection

SMUD Segmentation

The SMUD Segmentation targeting method was based on the finding that average savings of

pilot participants varied depending on which of eight market segments they were assigned. The

segmentation strategy, developed in 2007 specifically for SMUD, divided customers into eight

segments based on their frequency of past participation in SMUD programs, household income,

energy consumption, age of home, and other demographic information. The market segment

called “Big Toys, Big Spenders” had the highest average savings of the eight SMUD market

segments: this subgroup of pilot participants had average expected savings of 408 kWh per year.

Customers in this market segment tend to have children at home, higher household income, and

high electricity consumption.

This sub group of SMUD Segmentation yields 21 kWh savings per month which is equivalent to

1.7% savings. Savings is also decomposed on monthly basis. Savings are relatively stable across

all months. Table 2-8 summarizes the model results on the SMUD Segmentation. Graph 2-2

plots the monthly savings. Both point estimates and confidence intervals are plotted, with

confidence intervals defining the boundary of statistical estimates.

-50

-40

-30

-20

-10

0

10

De

lta

Mo

nth

ly k

Wh

UCLA (Savings Are Negative Values)

Point Est

Error Band

27

Table 2- 9: Saving Estimate of SMUD Segmentation


(%)



R-Squared 77%

Graph 2- 2: Saving Estimate by Month of SMUD Segmentation

High Use

The high usage targeting method simply selected from a pool of customers who had the highest

household electricity use, after excluding outliers and members of previously-selected test and

control groups. Based on pilot experience this group was predicted to save 360 kWh a year.

The sub group of High Use yields approximately 36 kWh monthly savings, which is equivalent

to 2.7% annual savings. Savings is also decomposed on monthly basis. Savings are relatively

stable but are somewhat seasonal across all months. Table 2-9 summarizes model result of the

High Use. Graph 2-3 plots the monthly savings. Both point estimates and confidence intervals

are plotted, with confidence intervals defining the boundary of statistical estimates.

-50

-40

-30

-20

-10

0

10

20

De

lta

Mo

nth

ly k

Wh

SMUD SEG (Savings Are Negative Values)

Point Est

Error Band

28

Table 2- 10: Saving Estimate of High Use


(%)



R-Squared 60%

Graph 2- 3: Saving Estimate by Month of High Use

E-Reports

The sub group of E-Reports yields 14 kWh monthly savings which is equivalent to 1.8% annual

savings. Savings is also decomposed on monthly basis. Table 2-10 summarizes model result of

the E-Reports sub group. Graph 2-4 plots the monthly savings. The different confidence interval

pattern may stem from the different usage patterns, such that the treatment group usage was

higher than OP control group in all months prior to program start. Both point estimates and

confidence intervals are plotted, with confidence intervals defining the boundary of statistical

estimates.

-70

-60

-50

-40

-30

-20

-10

0

De

lta

Mo

nth

ly k

Wh

High Use (Savings Are Negative Values)

Point Est

Error Band

29

Table 2- 11: Saving Estimate of E-Reports


(%)



R-Squared 82%

Graph 2- 4: Saving Estimate by Month of E-Reports

Seasonal Burst

The sub group of Seasonal Burst yields approximately 15 kWh monthly savings which is

equivalent to 1.2% savings annually. Savings is also decomposed on monthly basis. Table 2-11

summarizes model result on Seasonal Burst sub group. Graph 2-5 plots savings by month, where

owing to the relatively short period of study average monthly savings during the correspondence

period appears significantly higher, approaching 30 kWh. The corresponding data table can be

found at Appendix K-8.Both point estimates and confidence intervals are plotted, with

confidence intervals defining the boundary of statistical estimates.

-30

-25

-20

-15

-10

-5

0

5

De

lta

Mo

nth

ly k

Wh

E-Reports (Savings Are Negative Values)

Point Est

Error Band

30

Table 2- 12: Saving Estimate of Seasonal Burst


(%)



R-Squared 77%

Graph 2- 5: Saving Estimate by Month of Seasonal Burst

Predicted Saving vs. Measured Saving

The account level estimate is useful directing where marketing should focus on, but it is

essentially a different approach than the billing analysis from evaluation standpoint. Individual

account level savings is usually volatile and at best the average of all account be expected to

meet the saving from billing analysis. Only looking at top savers from account level estimates

assumes the same control group is used, which can be challenging. Moreover, the original

sample where the account level estimates were derived was not a truly random sample. Therefore

saving estimates may not extrapolate well to a larger population, or any sample that were

selected differently from the original sample.

We have seen cases where the out-of-sample applicability of individual account level saving is

minimal. Unless there is a way to collect all the behavior related changes, the out-of-sample

prediction power has been proved to be minimal, such that a customer might save in the first 3

months after receiving the report, but would gain or increase usage in the next 3 months. It is not

surprising though because these programs are designed to change behavior, and the constant

changing behavior makes it difficult to describe segments with behavior related terms.

For example, it may be possible that the SMUD Segment did not deliver as hoped for because

those big toy consumers may be relatively more elastic such that as the economy begins to

improve, they are the first to go back to high consumption habits. Another year has passed since

the conception of the strategy and the economy does show signs of improvement. Future study

-40

-35

-30

-25

-20

-15

-10

-5

0

201106 201107 201108 201109

De

lta

Mo

nth

ly k

Wh

Seasonal Burst (Savings Are Negative Values)

Point Est

Error Band

31

on how savings of the group change over time – did it start out higher than go down would be

useful to test this presumption.

Channel Effectiveness

One research objective of the billing analysis is to determine and compare the change in energy

use of recipients of paper reports versus recipients of electronic reports for participants selected

in the same manner. This objective can only be indirectly addressed by comparing coefficient

estimates of the five sub groups in Wave 2. It is noteworthy that paper reports and electronic

reports recipients are not selected in the same manner. Among the research sub groups of Wave

2, usage E-report recipients use much less energy than members of the other sub groups as

previously addressed in the section about control groups.

Because each sub group essentially targets a distinct customer segment whose usage is

significantly different from each other, it is not straightforward to compare impacts between

groups. Specifically because E-reports were sent to a different audience population than UCLA

selection, SMUD Segmentation, or High Use and Seasonal Burst, it is not straightforward to

compare impacts between E-reports to the other four strategies/segments using paper reports.

The five sub groups under Wave 2 (UCLA selection, SMUD Segmentation, High Use, E-reports

and Seasonal Burst) are all compared to the same Wave 2 random control group. The “Part”

variable represents the participation of the customer and is separated into either Paper or Email.

Variable “Email” equals one if an E-report recipient, 0 otherwise. The “Paper” variable is set to

one if the customer received a paper report 0 otherwise. The coefficient estimates in this case are

not savings; rather, they should be interpreted as an effect size which ranks the effectiveness

between paper versus email and the five sub groups. Table 2-12 summarizes the model results. It

appears that using email as a delivery channel and targeting strategy is not as effective as paper.

Paper reports appear to be approximately 3X more effective than email in contrast to the email

recipients, but note that the email recipients are also not “targeted.”

32

Table 2- 13: Effect Size of Email vs. Paper

Statistics Effect Size Pr > |t|

email -15.01 <.0001

Paper -47.44 <.0001


R-Squared 82%

Even though email does not yield as much savings as paper, it may still be a cost effective

channel, given it is probably less costly and less labor intensive to maintain email reports. The

team recommends a brief cost effectiveness analysis be conducted which incorporates program

costs, avoided costs and savings to more fully determine the cost effectiveness of each channel.

Effects for Wave 1 report Wave 1 reports started 4 years ago as SMUD operated this program in a pilot phase starting in

April 2008. Wave 1 started with two sub groups, one receiving monthly report, the other

quarterly. Saving estimates by year is summarized in Table 2-14. Saving estimates by month is

plotted in Graph 2-6. The corresponding data table can be found at Appendix K-9 and K-10.

Table 2- 14: Overall Saving Estimate of Wave 1


(%)



R-Squared 79%

Table 2- 15: Saving Estimate of Wave 1 by Year

Statistics Monthly Saving (kWh) Pr > |t| Percentage Saving (%)

2008 -17.28 <.0001 -1.82%

2009 -22.99 <.0001 -2.43%

2010 -22.54 <.0001 -2.38%

2012 -19.82 <.0001 -2.09%


R-Squared 79%

33

Graph 2- 6: Saving Estimate by Month of Wave 1

Persistence

In July, 2010, reports were halted to a portion of the remaining pilot group in order to test the

persistence of energy savings that may endure after recipients stop receiving reports. Before

reports were halted, this sub group yielded 22 kWh saving per month, equivalent to 2.3%

savings. After reports were halted, savings gradually dropped over the course of 12 months,

Yielding average monthly savings over the succeeding 12 months of 15 kWh, equivalent to

1.6%. The savings appears to be slowly degrading over time. Table 2-14 summarizes model

results via the coefficient estimates. Graph 2-7 plots the monthly saving. A 4th

order polynomial

trend line was fit which approximates the declining savings after reports were halted.

Extrapolating this trend line into the future suggests that savings persistence would be projected

to disappear approximately 24 months after the report delivery was halted. This trend line only

represents what the current data forecasts and would appear to be in conflict with the

Engineering and Survey findings that a significant fraction are structural – e.g. likely to persist.

Also it appears that summer of 2011 ended mildly with both monthly and persistence groups

having little savings compared to other months during treatment. This could cause the trend line

to over-fit the decay curve. The decay will also be influenced by the rate of adoption of efficient

appliances in the control group, e.g. as in phasing out incandescent. Longer observation is

needed to accurately quantify persistence.

-50

-40

-30

-20

-10

0

10

De

lta

Mo

nth

ly k

Wh

Wave 1 Over Time

point est Error Band

34

Table 2- 16: Saving Estimate of Persistence

Statistics Coefficient (kWh) Pr > |t| Percentage Saving (%)

Persistence (per Month) -14.93 <.0001 -1.58%

Saving Before Report Halted (per Month)

-21.78 <.0001 -2.30%


R-Squared 79%

Graph 2- 7: Saving Estimate by Month of Persistence

Monthly Reports

The sub group of Monthly Reports yields 27 kWh monthly savings which is equivalent to 2.4%

annually. Savings is also decomposed on monthly basis. Table 2-15 summarizes model results

for the Monthly Reports. Graph 2-8 plots the monthly savings.

Table 2- 17: Saving Estimate of Monthly Reports

Statistics Coefficient (kWh) Pr > |t| Percentage Saving (%) Estimated Monthly

Saving -27.31 <.0001 -2.39%


R-Squared 67%

-50

-40

-30

-20

-10

0

10

De

lta

MO

nth

ly k

Wh

Persistence Group Error Band Point Est Poly. (Point Est)

35

Graph 2- 8: Saving Estimate by Month of Monthly Reports

Quarterly Reports

The sub group of Quarterly Reports yields approximately 7 kWh savings per month which is

equivalent to 1.4% annually. Savings are also decomposed on monthly basis. Table 2-16

summarizes the model results for the Monthly Reports. Graph 2-9 plots the monthly savings.

Table 2- 18: Saving Estimate of Quarterly Reports

Statistics Coefficient (kWh) Pr > |t| Percentage Saving (%) Estimated Monthly

Saving -7.04 <.0001 -1.44%


R-Squared 67%

Graph 2- 9: Saving Estimate by Month of Quarterly Reports

-60

-50

-40

-30

-20

-10

0

10

De

lta

Mo

nth

ly k

Wh

Monthly Reports

point est Error Band

-20

-10

0

10

De

lta

Mo

nth

ly k

Wh

Quarterly Reports

Point Est Error Band

36

Chapter 3: Survey Analysis

This chapter addresses the research objectives within the survey analysis tasks. Specifically, IA

was asked to:

3.1. Estimate average electrical demand savings in kW for each year (Peak period: 4-7 pm,

Days: Three consecutive days of a heat storm preceding and including the day in which

the annual peak is set) based on estimates of how much of the kWh savings came from

specific end uses and measures and application of load shapes to be provided by SMUD.



3.3. Estimate amount of savings resulting from structural changes (i.e. equipment

replacement) vs. behavioral changes (i.e. how the equipment was operated).

Step One: Parallel Surveys (Online, Mailer and

Telephone) IA designed and administered three surveys which were implemented via internet, mailer and

telephone. All three surveys are based on the same questionnaire base which aims to track

actions taken during the program period. While implemented, format and wording were adjusted

to best fit the context of each survey channel (online, mailer and telephone). The purpose of

multiple survey channels is to control channel bias to the best extent possible. Survey sample

was split between the Home Electricity Report recipients versus control group.

Detailed breakdown of sample sizes and response rates of each survey channel are shown in

Table 3-1.

Table 3- 1: Sample Size and Response Rate of Each Survey Channel

Survey

Channel

Treatment

Group

Control

Group Total Completed

Response

Rate

Treatment

Rsp Rate

Control

Rsp Rate

Online N = 5,000 N = 6,500 N = 11,500 N = 497 4.3% 5.1% 3.8%

Mailer N = 1,000 N = 1,600 N = 2,600 N = 148 5.7% 5.4% 5.8%

Telephone N = 1,299 N = 1,143 N = 2,442 N = 155 6.3% 5.9% 6.9%

The surveys were developed based upon the energy savings tips presented in the HER provided

by SMUD. Treatment group members were asked to check energy saving actions they took due

to the Home Electricity Reports. Control group customers were asked to check energy saving

actions during the corresponding program period. Eighty five response options were provided in

checklist format. The response options were:

37

1. Clean or replace air filters

2. Set your thermostat wisely for comfort and

savings

3. Get the lint out

4. Light only needed areas

5. Only run washer with full loads

6. Switch to compact fluorescent bulbs

7. Let the sun in for warmth

8. Turn off lights in unoccupied rooms

9. Combine loads and only run full loads

10. Turn off your computer at night

11. Use cold water for laundry

12. Improve shading for windows and keep

blinds or drapes closed on sunny days

13. Use portable fans and ceiling fans instead of

AC to cool the house

14. Set refrigerator temperature wisely

15. Dry your towels and other heavy items in a

load separate from lighter-weight clothes

16. Use computer power-saving modes

17. Choose a more efficient television

18. Clear the area and keep all shrubs, trees, and

growth of any kind trimmed at least two feet

away from your air conditioner outdoor

condenser

19. Install efficient showerheads

20. Tune up your AC system

21. Let dishes air-dry instead of using the "heat-

dry" setting

22. Reduce water heater temperature

23. Make sure refrigerator seals are tight

24. Upgraded to a more energy efficient

refrigerator

25. Install a programmable thermostat

26. Choose LED holiday lights

27. Hang dry laundry

28. Use and switch off power strips

29. Turn down your thermostat when using your

fireplace

30. Maintain your furnace or boiler

31. Use indoor and/or outdoor light timers and

sensors

32. Clear areas around vents

33. Install a ceiling fan

34. Shave a minute off shower time

35. Upgraded to a more efficient clothes washer

36. Weather strip windows and doors

37. Clean refrigerator coils regularly

38. Use motion detectors outdoors

39. Upgraded to a more efficient dishwasher

40. Choose a laptop instead of a desktop

computer

41. Choose an efficient clothes dryer

42. Have you added attic insulation?

43. Choose an efficient water heater

44. Talk with your household members about

how they approach saving energy

45. Plant shade trees

46. Upgrade your central air conditioner or heat

pump to a more energy efficient model

47. Install efficient light fixtures

48. Use a whole-house fan instead of AC to cool

the house

49. Install dimmers on lights

50. Seal leaky ducts

51. Choose an efficient space heater and use

correctly

52. Did you participate in the SMUD

refrigerator recycling program?

53. Unplug stereos and other devices

54. Reduce pool pump run-time

55. Insulate water heater pipes

56. Select more efficient home office machines

57. Efficiently use electric blanket or heating

pads

58. Recycle your second refrigerator

59. Improve attic insulation

60. Install faucet aerators

61. Keep your water heater warm

62. Use solar outdoor lights

63. Turn off water heater when away

64. Upgraded to a more efficient furnace

65. Choose efficient ventilating fans

66. Adjust display setting on your TV to less

bright and save energy

67. Install window film to block or absorb light

and heat

68. Cool your home in zones

69. Have you added wall insulation?

70. Improve fireplace sealing

71. Upgraded to a more efficient heat pump

72. New Year's Resolutions for savings

73. Unplug your second refrigerator

74. Improve wall insulation

75. Unplug your cable or satellite box when not

in use

38

76. Install a cool roof with a metal or white roof

that has a special coating that allows it to

reflect light and heat

77. Find savings with SMUD online audit tool

78. Install a variable speed pool pump

79. Reduce pool temperature

80. Choose efficient room air conditioners

81. Save by covering your pool

82. Compare your appliances with a plug-in

power meter to learn how much electricity

each device uses

83. Install a solar water heater

84. Choose ductless air conditioning

85. Choose a ductless heat pump

39

Step Two: Nested Sample and Onsite Visits After completion of the three parallel surveys, a nested sample of survey respondents was

selected and recruited for an on-site visit. The purpose of the on-site visit was twofold: to verify

the accuracy of self-reported actions and to collect data for the engineering analysis, specifically

to develop building simulation models. A total of 406 customers were contacted for 30

completed on-site visits. Half of them were HERS recipients and half of them are from the

control group. The building characteristics and related engineering data were collected as

prototypes for follow-on building simulation models.

Differences between the Survey Sample and the Overall

Study Sample The survey sample was a subset of the overall study sample. Differences between these two

samples were examined in terms of annual and summer kWh for the two groups in the year prior

to the study. Results for the survey sample are shown in Table 3-2.

Table 3- 2: Differences between Survey Sample and Overall Study Sample


Program Mean

Control Group Pre-

Program Mean

Survey Pre-Program

Mean

Annual Use (kWh) 12,854 12,765 13,228

Summer Use (kWh) 3,575 3,541 3,663

In general, the survey sample includes higher energy users both annually (annual use is higher

than study sample treatment group by 2.9% (375 kWh), and higher than study sample control

group by 3.6% (464 kWh)) and in peak summer months (total summer use is higher than study

sample treatment group by 2.5% (88 kWh), and higher than control group by 3.4% (122 kWh)).

Survey respondents therefore appear to be customers who used more electricity both on peak and

annually than the mean of treatment and control groups. Billing analyses of higher users

exposed to comparison reports like HERS have generally shown (both in this study and others)

that higher users have both a greater potential and propensity to save. As a result it may be

reasonable to infer that the survey respondents represent a group with greater participation. This

concept is explored in greater detail in the next section.

40

Survey Findings In this section, we present survey findings for:

Item rankings by treatment and control group;

Billing analysis

Survey Item Rankings

Customers exposed to the report were asked to check what actions were taken because of the

report. As a result the ranked actions presented below show the most reported action to the least

reported action. This self-report listing should be interpreted with caution as it may be subject to

some desire to report actions that the respondents believe to be socially desirable or may be less

causally accurate than face value would suggest. Although the question was phrased to assess

what actions were taken as a result of the HERS the reader should bear in mind that it can be

challenging for respondents to accurately recall what motivated them to undertake a change over

a period of a year and a half. Still the rankings are relevant in that they reflect actions that

respondents recall and appear to be willing to engage in. The ranking is based upon the

percentages of each action checked by customers and is presented in Table 3-3. In this table a

label of “behavior” or “structural” is indicated. The team included all habitual “behaviors” in the

behavior category, while purchasing or structural changes were defined as “structural.” Thus,

turning off lights would be behavioral, while switching to CFLs would be classified as structural.

For equipment interactions the team designated many of those maintenance or control strategies

as behavioral in that they have the potential to be less persistent than new equipment. For

example thermostat set points can be over-ridden or air filters may not be consistently cleaned.

In general more survey respondents reported behavior changes (24.7%) than structural changes

(14.9%).

The top reported actions include prudent comfort control (set thermostat wisely, let sun in for

warmth, clear air filters on a regular basis), doing laundry in a more efficient way (getting lint

out of dryer, washing full loads) and switching to CFLs.

The top structural changes include switching to compact fluorescent bulbs (55%), choosing a

more efficient television (35%), making sure refrigerator seals are tight (30%) and, to a lesser

extent, using and switching off power strips (30%).

41

Table 3- 3: Survey Item Ranking from Top to Bottom

Category Description #

Reported Incidence Rate

Behavioral Set your thermostat wisely for comfort and savings 224 58.5%

Behavioral Get the lint out 220 57.4%

Behavioral Clean or replace air filters 219 57.2%

Behavioral Light only needed areas 214 55.9%

Structural Switch to compact fluorescent bulbs 212 55.4%

Behavioral Only run washer with full loads 202 52.7%

Behavioral Let the sun in for warmth 194 50.7%

Behavioral Turn off your computer at night 193 50.4%

Behavioral Turn off lights in unoccupied rooms 178 46.5%

Behavioral Combine loads and only run full loads 175 45.7%

Behavioral Use portable fans and ceiling fans instead of AC to cool the house

168 43.9%

Behavioral Use cold water for laundry 167 43.6%

Behavioral Improve shading for windows and keep blinds or drapes closed on sunny days

158 41.3%

Behavioral Set refrigerator temperature wisely 158 41.3%

Behavioral Dry your towels and other heavy items in a load separate from lighter-weight clothes

148 38.6%

Behavioral Use computer power-saving modes 136 35.5%

Structural Choose a more efficient television 132 34.5%

Behavioral Reduce water heater temperature 118 30.8%

Behavioral Clear the area and keep all shrubs, trees, and growth of any kind trimmed at least two feet away from your air conditioner outdoor condenser

115 30.0%

Structural Make sure refrigerator seals are tight 114 29.8%

Structural Use and switch off power strips 114 29.8%

Behavioral Let dishes air-dry instead of using the "heat-dry" setting 109 28.5%

Structural Upgraded to a more energy efficient refrigerator 105 27.4%

Structural Install efficient showerheads 103 26.9%

Behavioral Tune up your AC system 103 26.9%

Structural Choose LED holiday lights 102 26.6%

Behavioral Hang dry laundry 101 26.4%

Behavioral Talk with your household members about how they approach saving energy

101 26.4%

Behavioral Shave a minute off shower time 98 25.6%

Structural Upgraded to a more efficient clothes washer 97 25.3%

Behavioral Turn down your thermostat when using your fireplace 93 24.3%

Structural Weather strip windows and doors 92 24.0%

Structural Install a programmable thermostat 92 24.0%

42

Structural Upgraded to a more efficient dishwasher 91 23.8%

Structural Choose an efficient clothes dryer 90 23.5%

Behavioral Clear areas around vents 89 23.2%

Behavioral Maintain your furnace or boiler 88 23.0%

Behavioral Clean refrigerator coils regularly 88 23.0%

Structural Install a ceiling fan 87 22.7%

Structural Use motion detectors outdoors 87 22.7%

Structural Use indoor and/or outdoor light timers and sensors 86 22.5%

Structural Choose a laptop instead of a desktop computer 83 21.7%

Structural Did you participate in the SMUD refrigerator recycling program?

78 20.4%

Behavioral Plant shade trees 77 20.1%

Structural Choose an efficient water heater 77 20.1%

Structural Upgrade your central air conditioner or heat pump to a more energy efficient model

77 20.1%

Structural Install dimmers on lights 72 18.8%

Structural Have you added attic insulation? 72 18.8%

Behavioral Unplug stereos and other devices 67 17.5%

Structural Install efficient light fixtures 65 17.0%

Structural Use a whole-house fan instead of AC to cool the house 64 16.7%

Behavioral Reduce pool pump run-time 62 16.2%

Structural Insulate water heater pipes 59 15.4%

Behavioral Choose an efficient space heater and use correctly 58 15.1%

Structural Seal leaky ducts 54 14.1%

Behavioral Keep your water heater warm 53 13.8%

Structural Select more efficient home office machines 52 13.6%

Structural Recycle your second refrigerator 51 13.3%

Behavioral Efficiently use electric blanket or heating pads 50 13.1%

Structural Improve attic insulation 47 12.3%

Structural Install faucet aerators 42 11.0%

Behavioral Adjust display setting on your TV to less bright and save energy 42 11.0%

Behavioral Use solar outdoor lights 40 10.4%

Structural Choose efficient ventilating fans 39 10.2%

Behavioral Turn off water heater when away 38 9.9%

Structural Install window film to block or absorb light and heat 36 9.4%

Behavioral New Year's Resolutions for savings 33 8.6%

Structural Upgraded to a more efficient furnace 33 8.6%

Structural Improve fireplace sealing 32 8.4%

Structural Upgraded to a more efficient heat pump 32 8.4%

Behavioral Unplug your second refrigerator 25 6.5%

Behavioral Unplug your cable or satellite box when not in use 21 5.5%

43

Behavioral Find savings with SMUD online audit tool 21 5.5%

Behavioral Cool your home in zones 20 5.2%

Structural Have you added wall insulation? 20 5.2%

Structural Improve wall insulation 20 5.2%

Structural Install a cool roof with a metal or white roof that has a special coating that allows it to reflect light and heat

19 5.0%

Structural Install a variable speed pool pump 17 4.4%

Behavioral Compare your appliances with a plug-in power meter to learn how much electricity each device uses

15 3.9%

Behavioral Reduce pool temperature 13 3.4%

Structural Choose efficient room air conditioners 9 2.3%

Behavioral Save by covering your pool 8 2.1%

Structural Install a solar water heater 5 1.3%

Structural Choose ductless air conditioning 1 0.3%

Structural Choose a ductless heat pump 1 0.3%

Self-reported Action Error Rate This survey analysis is based on the self-reported actions collected from the three surveys. There

is a significant body of literature about measurement error which emerges during the self-

reporting process. Possible errors include not remembering behavioral or low involvement

actions, human error when filling out surveys, and an intention to guess the “right” answer,

among others. In the research team’s experience higher involvement activities tend to have more

accurate recall than lower involvement activities. For example accurate recollection of turning

on and off lights is more difficult to remember than installing weather-stripping or replacing a

refrigerator. In order to evaluate the quality of the survey data, on-site field analysts were asked

to observe and verify whether customers indeed adopted certain actions. Most investment type or

structural type of actions can be observed. For example if a customer reported adding wall

insulation, it can be observed and confirmed on site. Most behavioral type actions are more

challenging to directly observe. In this case the field analyst worked to verify the actions through

conversations with the customer.

On average the error rate is 3% which is possibly trivial. This error rate can be used to calculate

the probability as 100% – 3% =97% and measures how reliable the self-reported action is. For

example, we have 55% of the survey respondents who reported they switched to compact

fluorescent bulbs, for example, when adjusting for the error rate, decreases to 0.55 * 97% =

53% of survey takers who truly have switched to compact fluorescent bulbs. Given that 3% is

rather small, and perhaps not substantially significant, it was not used to adjust survey results.

44

Fixed Effect Model Results from the Survey Sample A fixed panel billing analysis was conducted based on the survey sample. Note there are 75

records out of the 79811

that are not included in the study sample, because they were from the

oversample beyond treatment and control group who also show significantly different usage

patterns. Therefore they are removed from this analysis. Billing analysis based on survey sample

yielded a net savings of approximately 24 kWh per month equivalent to 2.4% mean annual

savings. The estimated savings from the survey sample is higher than both Wave 1 and Wave 2,

which appears likely to be caused by the difference in the samples (e.g., survey respondents

appear to have been those that used more, and were more motivated to take actions to reduce

energy use) or over-stated the extent of their actions. Monthly saving of 24 kWh is equivalent to

an annual savings of 285 kWh. This number will be used in the section of engineering analysis

for calibration.

Table 3- 4: Saving from Survey Sample

Statistics kWh saving Pr > |t| Percentage Saving (%)

Saving (Survey Respondents)

-23.73 <.0001 -2.37%

Sample Size 723 homes

R-Squared 82%

11

There are two records from the original 800 respondents with account number missing and

cannot matched to the billing database. These two records are removed from the analysis.

45

Chapter 4: Engineering Analyses


3.1. Estimate average electrical demand savings in kW for each year (Peak period: 4-7 pm,

Days: Three consecutive days of a heat storm preceding and including the day in which

the annual peak is set) based on estimates of how much of the kWh savings came from

specific end uses and measures and application of load shapes to be provided by SMUD.



3.3. Estimate amount of savings resulting from structural changes (i.e. equipment

replacement) vs. behavioral changes (i.e. how the equipment was operated).

The engineering analysis consisted of a combination of building energy simulation modeling and

engineering algorithms. Building energy simulation modeling was applied to measures directly

affecting the HVAC end-use, such as shell measures, air leakage sealing, HVAC equipment

upgrades, duct tightening, and HVAC control strategies. Measures affecting lighting and

appliances were calculated using a combination of engineering algorithms and HVAC interactive

effects multipliers derived from the simulation models. Measures affecting domestic hot water

consumption were calculated using engineering algorithms. Detailed description of the

engineering approach can be found in Appendix V.

Simulation Modeling The building energy simulations were conducted using the DOE2.2 program. The building;

models were derived from residential building prototypes developed for the California Database

of Energy Efficiency Resources (DEER) project. The DEER prototype “model” in fact contains

4 separate residential buildings; 2 one-story and 2 two-story buildings. A scalable prototype was

developed from the one-story model. The scalable prototype retains much of the DEER

prototype model characteristics, but used the physical characteristics data from the 30 on-site

surveys. The onsite data collection form can be found in Appendix Q and R. The model is

“scaled” to the actual size of the surveyed building. Wall, roof, floor, window and door areas by

orientation are used to define the model. Insulation levels and glazing types are taken from the

survey. Lighting and appliance loads are based on the onsite survey data. HVAC system size,

thermostat settings, duct insulation levels and duct location are also taken from the survey data.

The scalable prototype was used to create a model for each onsite survey customer. Once the

model was developed, a series of simulations were done for each building. The simulations

covered the range of measures affecting the HVAC end-use, such as shell insulation, air leakage

sealing, duct insulation and sealing, HVAC system efficiency upgrades, thermostat schedule

46

adjustments and so on. HVAC interactive effects with internal loads were also calculated. Each

measure and building combination was run across 5 HVAC system types: Central AC with gas

heat, air source heat pump, central AC with electric heat, gas heat with no AC and electric heat

with no AC. These runs were done to calculate measure performance over the range of HVAC

system types represented in the surveys.

The simulations were driven by long-term average weather data from the California Energy

Commission for climate thermal zone (CTZ) 12. Coincident peak demand impacts were

calculated from the average of the simulated peak kW over the 4-7 pm peak period for three

consecutive days of a heat storm preceding and including the day in which the annual peak is set.

Using the CEC CTZ12 data set, the peak temperature of 103F occurred on July 24th

. The 1991

run year was used consistent with the DEER simulations. The peak day and the two preceding

days were weekdays, consistent with the SMUD peak demand definition.

Over 6,300 simulations were done across the combination of 30 buildings, 5 HVAC system types

and the measures relevant to each HVAC system type. These data were averaged across the 30

buildings to provide unit energy savings values by measure and HVAC system type. Unit energy

savings and peak kW savings values were developed for cooling, heating and HVAC system fan

end-uses. The end-use unit energy kWh and kW savings were applied to the building

characteristics data to develop energy and demand savings values by measure and end use for

each of the 800 surveyed households. The unit energy savings derived from the simulations are

shown in Appendix W.

Lighting and Appliances Unit energy consumption (UEC) data for lighting and appliances were taken from the Residential

Appliance Saturation Study (RASS) conducted for the California Energy Commission (CEC).

Since RASS results were reported for the California investor owned utilities (IOUs), values for

PG&E were used. UEC values from the RASS study are shown below:

47

Table 4- 1: Unit Energy Consumption from RASS

Appliance kWh/year Therm/year

Attic Ceiling Fan 96

Clothes Washer 121

Dishwasher 83

Dryer 719 26

First Refrigerator 827

Freezer 968

Home Office 89

Interior Lighting 1200

MicroWave 133

Miscellaneous 977

Outdoor Lighting 388

PC 673

Range/Oven 310 36

Second Refrigerator 1286

TV 738

Hourly load shapes for the lighting and appliance end-use was taken from the DEER prototype.

The weekday lighting and appliance load shape is shown in Graph 4-1 below:

Graph 4- 1: Weekday Lighting / Appliance Load Shape

48

Domestic Hot Water Energy and peak demand savings calculations for the domestic hot water end-use were

calculated using engineering equations. Since the domestic hot water system has no significant

interactions with the HVAC system, simulating the performance of the domestic hot water

system within DOE-2 was not necessary. The total hot water consumption was calculated as a

function of number of occupants in the home as shown in Table 4-2:

Table 4- 2: Domestic Hot Water Consumption as a Function of Number of Occupants

Number of occupants Gal/person-day12

1 29.4

2 22.8

3 20.6

4 19.5

5 18.9

6 18.5

The hourly domestic hot water load shapes were taken from the DEER prototype. The hourly

weekday load shape is shown in Graph 4-2.

12

Average hot water use per person taken from: Lutz, James D., Liu, Xiaomin, McMahon,

James E., Dunham, Camilla, Shown, Leslie J.McCure, Quandra T; “Modeling patterns

of hot water use in households;” LBL-37805 Rev. Lawrence Berkeley Laboratory,

1996.

49

Graph 4- 2: Weekday Domestic Hot Water Load Shape

Engineering Analysis Results

From the engineering analysis the kW/kWh ratio was calculated. This ratio measures the kW

impact scaled to kWh saving. It is multiplied by 285 kWh which is the estimated annual kWh

savings from the previous section of billing analysis on survey data. The estimated kW

reduction is approximately 0.08 kW (80 Watts), half of that is HVAC. The kW/kWh ratio was

also calculated for customers who were targeted with the Seasonal Burst strategy, and then

multiplied by corresponding estimated annual kWh saving from this sub group (177 kWh). The

kW reduction from the Seasonal Burst sub group is slightly lower or approximately 0.06 kW (60

Watts).

The percentage savings and kWh savings were also decomposed into end use level as detailed in

Table 4-3 and Graph 4-3 below.

50

Table 4- 3: Saving by End Use

End Use Percentage Saving Annual kWh Saving

Lights and Appliance 58.2% 165.9

Electric Heat 6.9% 19.7

Cooling 26.9% 76.6

Fans 5.3% 15.1

Water Heat 2.7% 7.7

Graph 4- 3: Saving by End Use

Looking further on the “Lights and Appliance” portion of the pie chart, we further decompose

this portion into Lighting, Appliances, Plug Loads and Pool / Spa. What these results illustrate is

that one of the most commonly reported actions (install CFLs) is not one of the more impactful

(from a kWh perspective). Instead willingness to change out pool and spa equipment is a

significant component of the ability to realize savings. (This is similar to the saving that are

realized from the behavior aspects where the popular action changing dryer lint is nowhere near

as impactful as changing pool pump run times.)

58%

7%

27%

5% 3%

kWh Saving Attributable to End Use

Lights and Appliance Electric Heat Cooling Fans Water Heat

51

Graph 4- 4: Saving by End Use within Lights & Appliances

Decomposing measures into behavioral measures versus structural measures, approximately 60%

of kWh saving comes from behavioral savings actions which is equivalent to a monthly savings

of approximately 11 kWh and an annual saving of 129 kWh. By contrast 40% of kWh savings

comes from structural measures which are equivalent to a monthly saving of approximately 9.5

kWh and an annual saving of 114 kWh. Table 4-5 shows the percentage saving and annual kWh

savings down to individual measure level.

Table 4- 4: Saving by Behavioral vs. Structural Change

Action Type Percentage Saving Annual kWh Saving

Behavioral 59.4% 169.3

Structural 40.6% 115.7

Graph 4- 5: Saving by Behavioral vs. Structural Change

39%

21%

25%

15%

Lights & Appliances: Break Down

Appliances Lighting Plug Loads Pool/Spa

59%

41%

Behavioral vs. Structural kWh Contribution

Behavioral Structural

52

Table 4- 5: Saving by Individual Measure

Type Description kWh Saving Percentage

Annual kWh

Saving

Behavioral Set your thermostat wisely for comfort and savings 13.0% 37.2

Behavioral Reduce pool pump run-time 7.3% 20.9

Structural Upgraded to a more energy efficient refrigerator 5.4% 15.3

Behavioral Hang dry laundry 4.1% 11.6

Behavioral Use computer power-saving modes 4.1% 11.6

Behavioral Improve shading for windows and keep blinds or drapes closed on sunny days

3.7% 10.7

Structural Recycle your second refrigerator 3.7% 10.6

Structural Upgrade your central air conditioner or heat pump to a more energy efficient model

3.6% 10.3

Behavioral Turn off your computer at night 3.6% 10.2

Structural Use a whole-house fan instead of AC to cool the house 3.3% 9.4

Behavioral Light only needed areas 2.9% 8.3

Behavioral Get the lint out 2.7% 7.6

Behavioral Turn off lights in unoccupied rooms 2.4% 6.9

Structural Switch to compact fluorescent bulbs 2.2% 6.4

Structural Choose a more efficient television 2.2% 6.3

Structural Choose a laptop instead of a desktop computer 2.2% 6.3

Behavioral Use portable fans and ceiling fans instead of AC to cool the house

2.1% 6.0

Structural Upgraded to a more efficient clothes washer 2.0% 5.8

Behavioral Unplug your second refrigerator 1.8% 5.1

Structural Choose LED holiday lights 1.7% 4.9

Structural Install a variable speed pool pump 1.7% 4.8

Structural Improve attic insulation 1.7% 4.8

Structural Use motion detectors outdoors 1.5% 4.3

Behavioral Let the sun in for warmth 1.3% 3.8

Behavioral Clean or replace air filters 1.3% 3.6

Behavioral Turn down your thermostat when using your fireplace 1.2% 3.5

Structural Use and switch off power strips 1.2% 3.5

Structural Improve wall insulation 1.1% 3.2

Behavioral Efficiently use electric blanket or heating pads 1.1% 3.2

Structural Use indoor and/or outdoor light timers and sensors 0.9% 2.7

Behavioral Combine loads and only run full loads 0.9% 2.6

Structural Install a ceiling fan 0.8% 2.3

Behavioral Cool your home in zones 0.7% 2.1

Behavioral Tune up your AC system 0.7% 2.0

53

Structural Weather strip windows and doors 0.7% 2.0

Structural Seal leaky ducts 0.7% 1.9

Behavioral Use cold water for laundry 0.7% 1.9

Behavioral Set refrigerator temperature wisely 0.6% 1.8

Structural Install window film to block or absorb light and heat 0.6% 1.7

Behavioral Dry your towels and other heavy items in a load separate from lighter-weight clothes

0.6% 1.7

Behavioral Unplug stereos and other devices 0.5% 1.6

Behavioral Reduce water heater temperature 0.4% 1.2

Structural Install a solar water heater 0.4% 1.2

Structural Make sure refrigerator seals are tight 0.4% 1.1

Structural Choose efficient ventilating fans 0.4% 1.1

Behavioral Clean refrigerator coils regularly 0.4% 1.0

Behavioral Adjust display setting on your TV to less bright and save energy

0.3% 0.9

Structural Insulate water heater pipes 0.3% 0.9

Structural Select more efficient home office machines 0.3% 0.9

Behavioral Let dishes air-dry instead of using the "heat-dry" setting 0.3% 0.9

Structural Install efficient light fixtures 0.3% 0.8

Behavioral Clear the area and keep all shrubs, trees, and growth of any kind trimmed at least two feet away from your air conditioner outdoor condenser

0.3% 0.8

Structural Install efficient showerheads 0.3% 0.8

Structural Install dimmers on lights 0.2% 0.6

Structural Upgraded to a more efficient dishwasher 0.2% 0.6

Structural Install faucet aerators 0.1% 0.4

Structural Choose an efficient water heater 0.1% 0.3

Structural Install a cool roof with a metal or white roof that has a special coating that allows it to reflect light and heat

0.1% 0.3

Behavioral Only run washer with full loads 0.1% 0.2

Behavioral Shave a minute off shower time 0.1% 0.2

Behavioral Unplug your cable or satellite box when not in use 0.1% 0.2

Structural Choose efficient room air conditioners 0.037% 0.105

Structural Choose ductless air conditioning 0.012% 0.035

Structural Improve fireplace sealing 0.009% 0.025

54

Chapter 5: Impact of HERS on Other SMUD Programs


3.4. Determine the percentage of savings attributable to actions taken with support from

other SMUD programs.

3.5. Determine increase in participation of targeted programs and services.

The research objective “Determine the percentage of savings attributable to actions taken with

support from other SMUD programs” implies some synergistic effects between the Home

Electricity Report and the other SMUD rebate and financing programs. From a billing analysis

stand point, it is important to differentiate between double counting energy savings and

synergistic benefits. It is important to control for the impact from structural changes caused by

other SMUD rebate and financing programs so that the savings associated with the Home

Electricity Report is indeed the savings caused by the report, not the other programs, and

therefore not double counting. On the other hand if a Home Electricity Report recipient was

motivated by the reports and decided to participate in additional change this contributes

additional savings that should be captured. It is important to net out these countervailing factors

overall. This differentiation is carried out in this section, such that the percentage of savings

attributable to other SMUD programs is the synergistic effect. Note that all fixed effect panel

models developed so far include the term of other program participation; therefore double

counting has been avoided. That is to say, we have netted out savings caused by structural

change from other programs. However we captured the incremental behavioral savings due to

someone getting both a report and enrolling in a program. Also of interest was determining

whether the Home Electricity Report program had any effect on driving customers into SMUD’s

rebate and financing programs.

Determining the contribution of SMUD’s rebate and financing programs on energy savings

attributed to the Home Electricity Report program was accomplished by adding a synergy effect

term, which is a 3-way interaction term of treatment membership, post-program period, and

participation in rebate and financing programs in post-program period. This additional term

measures the additional savings achieved if a customer was motivated by the reports and ended

up participating in some SMUD rebate and financing program.

Determining whether the Home Electricity Report program had any effect on driving customers

into other SMUD’s programs was accomplished by examining whether the participation rates for

such SMUD programs had changed differentially over time for the Home Electricity Report

program treatment and control groups.

55

The Synergy Effect of Joint Program Participation on

Energy Savings Approximately 8,600 (or 15 percent) of recipients of Home Electricity Reports also participated

in SMUD’s rebate and financing programs in either pre-program or post-program periods. Out of

the 8,600 recipients, there were approximately 4,500 (or 8 percent) of them who participated in

some SMUD rebate and financing program after report inception across multiple years. Both

Wave 1 and Wave 2 customers are taken into consideration to form a complete view of the

synergy effect. To control for the long time lag between the two Waves and to reduce error

variance when mixing distinct segments, the natural log of kWh instead of kWh was used. To

control for impact from other programs and to differentiate the synergy effect, the evaluation

team re-ran the fixed effects panel model by including an interaction term of treatment

membership, post-program period and participation dummy during post-program period. This

variable measures the additional saving a customer will achieve if they participate in rebate and

financing programs AFTER they received Home Electricity Reports. That is to say, if overall

saving is approximately 2%, if a customer took advantage of rebate and financing programs they

are expected to achieve additional 0.64% saving. At the end this person will save 2.64% in total.

We assume such customers participated in rebate programs because of the reports. Table 5-1

summarizes the model results.

Table 5- 1: Synergy Effect

Statistics kWh saving Pr > |t| Percentage Saving (%)

Monthly kWh Saving -6.55 <.0001 -0.64%


R-Squared 82%

Estimated savings from the synergistic effect is approximately 6.5 kWh per month, equivalent to

0.6%. Divided by the weighted average of savings from Wave 1 and Wave 2 at 19.76 kWh,

about 33 percent of the savings attributed to the Home Electricity Reports can be accounted for

by the financial support provided by SMUD’s rebate and financing programs designed to help

residential customers purchase energy efficient products and services. Note the saving estimates

reported in the previous section are essentially a weighted average of customers who did not

participate in rebate programs and customers who did participate in rebate programs. Given the

relatively small participation rate as detailed in next section, saving without support from other

programs is very close to what have been reported. Therefore the synergy effect can be viewed

as an adder. The fixed effects panel model results for this analysis can be found in Appendix L.

56

Rebate/Finance Program Participation Rates Did receipt of the Home Electricity Reports promote participation in other SMUD rebate and

financing programs? That is, we wanted to know whether the Home Electricity Report recipients

participated in subsequent SMUD rebate and financing programs at a more accelerated rate

relative to the control households in order to find out if some of the energy efficiency changes

they might have become aware of emanated from their exposure to the Home Electricity Reports.

We examined this question by tracking the start date of each SMUD program for treatment and

control group households who participated in the SMUD rebate and financing programs during

the years prior to the Home Electricity Report pilot program (April 2007 through March 2008 for

Wave 1, October 2009 through September 2011 for Wave 2) compared to the years after

program inception. In this analysis, households that received multiple awards in either time

period were counted only once. We first looked at overall take rate of any SMUD rebate and

financing programs, and then further examined each individual program. This analysis was

conducted on each SMUD rebate and financing program for a total of 22 measures listed below:

1. AC PKG

2. AC SPLIT

3. ASSESSMENT PERF

4. ASSESSMENT PRES

5. CEILING INS

6. CLOTHES WASHER

7. COOL ROOF

8. DISHWASHER

9. DUCT IMPROVE

10. HEAT PUMP PKG

11. HEAT PUMP SPLIT

12. PERFORMANCE

13. PERSCRIPTIVE

14. POOL PUMP 2SP

15. PV FINANCING

16. REFRIGERATOR

17. ROOM AC

18. SIDING

19. SOLAR WH

20. WALL INS

21. WHOLE HOUSE FAN

22. WINDOWS

Table 5- 2: Joint Program Participation

57

Time Frame Control Group Treatment Group Percent

N % N % Difference13

Pre-Program 3,024 4.4 1,983 4.2 -0.2

Post-Program 2,306 2.9 1,682 3.1 0.2

The data in Table 5-1 show that participation rates for the SMUD rebate and financing programs

were similar (4.4 percent vs. 4.2 percent) for the treatment and control households during the

year prior to the Home Energy Report pilot program. However, participation rates declined for

both groups during the years of the pilot program.14

The economy has been weak since 2008, and

discretionary dollars available for energy efficiency might be shorter supply, which could help

explain the decline in participation in the rebate and finance programs. Nevertheless, it should be

noted that the participation rate for the treatment group was higher than that of the control

group in the post program years: 3.1 percent versus 2.9 percent. Thus, the recipients of the

Home Electricity Reports maintained a greater level of involvement in the SMUD rebate and

financing programs compared to similar households who did not receive the Home Electricity

Reports during a time of economic stress in California. In the end the relevant comparison is the

DiD, which equals 0.4% greater net participation in other programs for report recipients across

the time period.

Zooming further into individual program levels, the percentage change of each program in the

pre- and post period by treatment and control group was evaluated. Specifically program take

rates in pre-program months are calculated by treatment group and control group; meanwhile

program take rates in post-program months are calculated by treatment group and control group.

Take rate differences were then calculated on treatment groups as well as control groups. In the

end the difference in difference (DiD) between treatment group and control group was calculated

to determine the programs whose take rate was accelerated by Home Electricity Report during

the program months. There are a total of eight out of the twenty two measures which were

accelerated by receipt of the Home Electricity Report, however this effect was quite small for

most of the programs and a few measures exhibited reduced uptake following report receipt.

These two measures with small but appreciable lift were:

REFRIGERATOR

AIR CONDITIONING SPLIT SYSTEM

13

The percentages in Table 5-2 are derived from a control group base of 69,366 households and a

treatment group base of 54,775 households.

58

Table 5- 3: Joint Program Participation by Individual Program15

Program Treatment

Pre-Program Treatment

Post-Program Control Pre-

Program Control Post-

Program DiD (T-

C)

REFRIGERATOR 667 121 988 148 0.21%

AC_SPLIT 567 564 705 628 0.11%

HEAT_PUMP_SPLIT 146 138 208 155 0.06%

WHOLE_HOUSE_FAN 239 200 311 229 0.05%

HEAT_PUMP_PKG 38 42 41 40 0.01%

COOL_ROOF 9 17 12 17 0.01%

POOL_PUMP_2SP 39 14 50 16 0.00%

ROOM_AC 48 25 65 35 0.00%

ASSESSMENT_PERF 110 116 0.00%

ASSESSMENT_PRES 28 20 0.00%

PERFORMANCE 19 18 0.00%

PERSCRIPTIVE 26 33 0.00%

PV_FINANCING 4 5 7 9 0.00%

SOLAR_WH 22 13 28 18 0.00%

WALL_INS 3 0 3 1 0.00%

SIDING 14 3 11 4 -0.01%

DUCT_IMPROVE 36 19 41 27 -0.01%

CEILING_INS 11 1 9 4 -0.01%

CLOTHES_WASHER 141 149 156 175 -0.01%

DISHWASHER 80 55 93 74 -0.02%

WINDOWS 207 83 236 105 -0.04%

AC_PKG 169 169 217 245 -0.04%

Taken together, the data in Table 5-1 suggest that a somewhat greater proportion of Home

Electricity Report recipients became involved in the SMUD rebate and financing programs

compared to control households, perhaps as a way to support the implementation of energy

efficiency changes that may have been prompted by the information contained in the Home

Electricity Reports. However the effect is relatively modest and indeed most of the lift is not

statistically significant. We do believe this is an area where further research is needed and the

causal link (as opposed to correlation) can only be assumed here. Thus, exposure to the Home

Electricity Reports appears to relate in weak positive manner on a few SMUD rebate and

financing programs, but further research is needed to clarify and confirm this finding.

15

Multiple program participation was also examined whereas few data points were available

(<100 data points) and therefore results not included.

59

Chapter 6: Conclusions and Recommendations

Based on the foregoing research IA makes the following conclusions:

The analyses show that the reports do appear to be correlated in a net reduction in energy

use however the synergistic effect of SMUD incented efficiency rebate programs and the

reports did represent a potentially significant fraction of savings (as much as one-third).

The results presented below have removed double counting for SMUD programmatic

actions but not for the synergistic effect. Note, too, that synergistic effects are possible in

both directions. The Home Energy Report may spark subsequent participation in rebate

programs or participation in rebate programs may motivate more contemplation and/or

specific followup on home energy report recommendations.

We assessed eight research groups: Monthly and Quarterly Reports from Wave 1

(beginning April 2008) as well as a persistence group from that Wave. Five additional

subgroups from a second Wave (beginning October 2010) were also assessed these

include: UCLA, SMUD Segmentation, High Use, E-reports and Seasonal Burst were also

assessed. Overall savings using a weighted average of these 8 research groups yields a

monthly saving of 20 kWh per customer household and 1.9% average savings annually.

Wave 1 generated greater savings in the aggregate than Wave 2 (2.2 versus 1.6%).

o The strongest performing of the subgroups included the High Use subgroup in

Wave 2 which had 2.7% annual savings or 36kWh mean monthly savings. The

Monthly group from Wave 1 also performed strongly at 2.4% savings or 27 kWh

mean monthly savings. The UCLA subgroup also performed well with 2.2%

savings or 22 kWh mean monthly savings.

Though less impactful the Seasonal Burst results are promising from a

cost effectiveness stand point in that they delivered more than half the

average impact of all the Waves (15 kWh mean monthly equivalent to

1.2% of usage) with only one third of the reports (four seasonally

appropriate mailings). The seasonal burst segment has peak savings at

0.06kW (comparing to peak saving of 0.08 kW of the full report program.

o Even though a quarter of survey respondents reported behavioral savings as

opposed to structural (approx. 15%), structural changes nonetheless account for

40% of the savings presented by the program and likely contribute to the

persistence observed to date.

o Interestingly the SMUD Segmentation Group performed less well at 1.7% annual

savings or 21 kWh mean monthly savings.

60

o The Persistence subgroup was estimated to be 15 kWh per month during the 12

month period after reports were halted, equivalent to 1.6% saving annually.

Extrapolating we expect the savings from this group to persist nearly 2 years

following the last report delivery. This result may seem surprising at first but

when SMUD program participation and self- reported structural changes are

considered, the result has good face validity. More research is needed to confirm

how long the effect will last.

o Electronic report recipients did not appear to have as strong a response at only

1.8% savings or 14 kWh mean monthly. This result may be somewhat unreliable,

given that the the recipients of electronic reports were not selected in exactly the

same manner as recipients of paper reports.

o Respondents do appear to express preference for some types of actions. The most

frequently reported actions include: prudent comfort control (set thermostat

wisely, let sun in for warmth, clear air filters on a regular basis), doing laundry in

a more efficient way (getting lint out of dryer, washing full loads) and switching

to CFLs.

o The top structural changes reported include switching to compact fluorescent

bulbs (55% of those responding), choosing a more efficient television (35%),

making sure refrigerator seals are tight (30%) as well as using and switching off

power strips (30%).

Exposure to the Home Electricity Reports appears to have had a weak positive influence

on participation in the SMUD rebate and financing programs. Prior to report exposure,

program take rate in the control group was slightly higher than in the treatment group

(4.4% vs. 4.2%). After report exposure the take rate of treatment group became slightly

higher than control group (3.1% vs. 2.9%). Individual program lift can be found at Table

5-3.

The research team also assessed the two implementer selected control groups. By

examining pre-program usage level and demographic factors such as home age and home

size, we discovered that each treatment and control group from Wave 1 and 2 had

detectable differences in aggregate usage, summer usage and home age.

o Wave 2 control groups were selected based on a randomized experiment so that

the magnitude of annual use difference is smaller than Wave 1.

o More challenging are the E-report recipients which were compared to a pure

random control sample which is not 100% Your Account users. The estimated

saving of the E-report group may not represent the true saving given control

group is drawn from a different population than treatment group.

Based on the foregoing research IA makes the following recommendations:

61

Because there appears to be a synergy between SMUD program participation and report

receipt (perhaps as much as 1/3 of savings may be related to synergies in that group), it

could be cost effective to use the reports as a “reward” for participating in SMUD rebate

programs. Alternatively, the receipt of the report following participation in a SMUD

rebate program may generate more awareness and contemplation on the part of

customers, assuming that a post-participation mindset is primed for enhanced

consideration of report recommendations, versus a potentially superficial read on the part

of other customers that had not recently participated in other SMUD programs. By

sequencing the reports to build on program participation, it would appear that some

additional savings can be realized.

Additional offer sequences may be considered based on this research. For example those

seasonal burst households which represent relatively high users, on peak, did not save as

much (kW) as the aggregate treatment group (0.06 kW as opposed 0.08 kW). While this

effect is relatively modest, it does suggest that high seasonal users are comprised of

homes that may have either older equipment or thermal shells with less integrity. This

suggests that they are a particularly high value group for insulation offers, or

weatherization, in that they have a demonstrated willingness to reduce use but may be

limited by their “more leaky” shell characteristics or “older, less efficient” equipment.

o In effect, an efficient appliance that is also used or operated more efficiently may

generate more savings than one that is installed and not used properly, or as

designed. This might include “take back” effects from customers’ setting

thermostats at more comfortable setpoints versus previous equipment, or ignoring

annual maintenance versus past behaviors with older equipment (assuming newer

equipment is “clean”). This suggests that report recommendations might

consider guiding customers, in this regard, where post participation outreach is

possible through the reports. This strategy might strengthen the captured savings

among customers within other SMUD programs, leading to an increased savings

overall.

o This strategy could be combined with forward implementation plans to leverage

customer insights and improve customer rapport as well. In effect, tips should be

presented that relate to the usage of a new appliance, perhaps during the first 6

months to support efficient use habits “from the get go” rather than working hard

to overcome or “break bad habits” later.

o Best efforts were made to use available SMUD participation data when

partitioning the billing analysis into actions that participants reported taking.

SMUD might consider performing additional data quality checks within

participation databases to mitigate the possibility of small but potentially

consequential errors that could result from missing program data, as well as to

further explore interaction effects between different types of programs more

closely. This might include addressing the possible negative interaction effects

arising from prior DR participation (as opposed to EE). It may be that direct load

control customers’ KW impacts are substantially different than non load control

populations, or that load control executions differ across pre and post program

62

evaluation periods. For example, if a direct load control household changes their

set point as a result of the home energy reports, the load shed impacts during

event days might be lower. Or, if a customer participates in load control during

the pre evaluation period and not during the treatment period, then the KW

impacts might be higher. By more clearly defining the expected impacts and

operations from other SMUD programs, SMUD will be better positioned to

estimate these possible interaction effects, and their resulting contribution, if any,

to the home energy report impacts or program promotion sequencing

opportunities.

It can be difficult to assess the causal link between a generic tip presented in a home

energy report and other sources of efficiency and conservation knowledge in the market

place. However, augmenting participation databases with other secondary promotional

activity of which customers may be aware, SMUD will be able to better discern direct

program impacts from secondary sources of awareness or action.

o Tips should be more finely tuned and targeted so that existing customer usage and

prior participation knowledge are both leveraged to a greater extent. For example,

customers with little, or no, peak demand should not be given thermostat set point

tips. Arguably, this is a waste of precious space within the report. Moreover, this

type of tip might cause a loss of credibility within the mind of the customer, to the

extent that the tip is not personalized for their home, and jeopardizes the

credibility of tips that might otherwise be more well received. Similarly, a

customer with no second refrigerator (if a customer profile from online audit

exists) should not be messaged about refrigerator recycling. Although increased

personalization may be difficult in many cases, it is exactly this type of

personalization that is the core intent of these types of home energy report

activities and failed personalization attempts may “turn off” customers not only

now, but for years to come. Within other industries, firms usually in-source these

types of personalized customer relational interactions, for use in personalized

marketing and targeting, versus conducting this type of activity as a “one-shot”

out-sourced activity. In their strategic view, this type of personalized marketing is

a core strength and part of ongoing marketing operations more than simply a one

time marketing tactic. But to achieve increased personalization and credibility in

customers’ minds, more detailed database management and promotion tracking is

required as a part of ongoing customer service operations.

o Using more vivid and colorful language in tips will not only generate better

customer recall but also help SMUD in future research efforts to identify where a

survey respondent heard of the tip. IA has conducted past analyses and designed

experiments on how to analyze tip efficacy, so that each tip on a report can be

quantified for effectiveness, leading to higher long runROI. In some cases,

simple word additions, or phrasing changes or modifications has resulted in

observed response differences of up to 300%. Similarly in future studies

respondents could be asked about “fake” tips or tips that were not presented to

them, to assess social desirability response bias, or to normalize the treatment

effect for potential non sampling biases. Within other industries, firms often use

63

this type of tactic to normalize survey response biases for issues related to brand

awareness or advertising recall.

Other research conducted by IA and others, in the past, has revealed that customers are

often willing to engage with their utility provider to obtain “a better” or more customized

analysis, either through additional online interactions or the provision of web based

home audit or home characteristics information. This additional information represents a

possible mechanism to engage with those customers that are not saving as a result of the

report, or worse, where usage is actually increasing post report receipt. If a respondent is

confronted with information that they do not want to attend to, they often look for reasons

to disbelieve or refute the source. As such SMUD may wish to consider engaging with

non-savers in a different manner, with some set of alternatives to the traditional tips, to

assess in what ways SMUD can reach these customer segments in more meaningful

ways. This might include the provision of more information on issues related to comfort,

convenience, billing, outages, or power quality alongside the normal energy efficiency

tips (albeit fewer of them, given the limited report space). This approach might be more

interesting and insightful to some non savers, leading to increased report credibility and

trust with the utility.

It may be worthwhile to assess whether, on balance, lower impact actions (but widely

adopted) create more impact than higher impact actions that are less widely adopted.

Consider the notion of behavioral plasticity. These indicate for example that carpooling,

though very impactful in saving gasoline, suffers from low adoption rates. However

purchasing more efficient tires, while less impactful, is more readily adopted. As a result

the net impact is greater. The current analysis indicated a similar finding. Most of the

structural energy savings that were reported appeared to come from refrigeration

upgrades not from the more commonly reported CFL change outs. This triggers notions

of self-efficacy. If customers do not see appreciable savings on their bill from these set

point changes, it is likely that comfort could trumps modest bill savings in the long run,

while perhaps more modest lighting or other minor actions persist. By combining

targeted tips with targeted promotions of weatherization or AC tune up programs in

combination to this type of customer segment, it is possible that more significant energy

savings may result, and persist.

o Now that the engineering models for the SMUD area have been populated, it may

be worthwhile to assess whether some households revealed actions (e.g. January

savers) are correlated with structural components (electric heat) to further enhance

or develop future targeting opportunities..

o In addition it may be worth exploring whether a particular type of customer is

willing to make AC set point changes and to assess whether insulation or AC

offers should be targeted in combination with this, to yield additional savings.

Given the synergistic findings revealed within our analysis, this type of targeting

refinement is likely to be beneficial. In addition, IA has successfully conducted

past analyses which uses billing history, local weather, and

household/demographic information to quantify the sources of individual home

inefficiencies (e.g., older AC, leaky shell, large family, etc) in an effort to more

64

precisely and credibly target the promotion of specific types of energy efficiency

and load control programs. SMUD could apply similar types of analytics to more

accurately identify and target “leaky shell” homes with combined promotions of

both the home energy report and SMUD’s weatherization or shell improvement

measures, jointly, to increase overall savings. .

In light of the relatively modest performance of the SMUD segmentation group,

additional segmentation hypotheses based on revealed preferences should be explored.

o These results would assess the characteristics of responders and nonresponders to

better integrate existing qualitative segmentation research at SMUD.

o E Reports in particular may represent a unique customer group. Better

experimental designs can improve the assessment of the impact of self nominated

channel preferences (e.g. do customers respond with more impact if their channel

preference is respected? If no, do they appear to have greater satisfaction, or other

engagement, such that future asks are “potentiated” or enhanced).

o Seasonal burst responders should be further analyzed using demographic

information or insights, focus groups, or similar research, so that more of these

potentially cost effective households can be identified and targeted.

The persistence studies should be reassessed in one years’ time. During the intervening

12 months, program participation should be carefully tracked within this group so that

structural changes, and other promotional history, from SMUD incented programs can be

parsed out of the savings impacts.

o In the future SMUD may also wish to further assess whether any of the self-

reported structural changes were the result of other non-SMUD programs such as

Federal Tax Rebates or PG&E gas related measures. The rationale for doing so is

similar to the adoption window logic described above. After undertaking a capital

investment to improve efficiency structurally there may be a period of capital

depletion, where follow on offer uptake is not high, but receptiveness to

behavioral tips or equipment operational suggestions is more pronounced.

o SMUD may wish to conduct a separate billing analysis on those homes that

responded to the surveys and engineering studies in this report. It’s likely that

those homes that indicated that they “took action” in response to the report are

more compliant with those actions than those that did not respond to the survey.

In effect the very act of responding to the survey may have reinforced their

commitment. A finding such as this would not only be consistent with the

research literature, it would also provide insights into the types of future

engagement that SMUD may wish to take following the E reports.

65

APPENDIX A: OVERALL WAVE2 SAVING

Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 55540 336823847225 6064527.3177 85.00 <.0001 Error 1.23E6 87779502343 71350.830841 Corrected Total 1.29E6 424603349568 R-Square Coeff Var Root MSE kwh Mean 0.793267 23.83618 267.1158 1120.632 Source DF Type I SS Mean Square F Value Pr > F cont_acc 55508 312321584860 5626604.9013 78.86 <.0001 M200910 1 340258215.59 340258215.59 4768.81 <.0001 M200911 1 147209787.5 147209787.5 2063.18 <.0001 M200912 1 1306416796.7 1306416796.7 18309.8 <.0001 M201001 1 592836722.98 592836722.98 8308.76 <.0001 M201002 1 870098030.05 870098030.05 12194.6 <.0001 M201003 1 422182497.57 422182497.57 5916.99 <.0001 M201004 1 1823187223.4 1823187223.4 25552.4 <.0001 M201005 1 1412781743.7 1412781743.7 19800.5 <.0001 M201006 1 45085944.732 45085944.732 631.89 <.0001 M201007 1 3669599379.1 3669599379.1 51430.4 <.0001 M201008 1 1218748872.9 1218748872.9 17081.1 <.0001 M201009 1 41310317.565 41310317.565 578.97 <.0001 M201010 1 309925137.24 309925137.24 4343.68 <.0001 M201011 1 352604755.53 352604755.53 4941.85 <.0001 M201012 1 418008130.74 418008130.74 5858.49 <.0001 M201101 1 344750305.08 344750305.08 4831.76 <.0001 M201102 1 694295364.14 694295364.14 9730.73 <.0001 M201103 1 408350366.78 408350366.78 5723.13 <.0001 M201104 1 3207061979.1 3207061979.1 44947.8 <.0001 M201105 1 4119912652.8 4119912652.8 57741.6 <.0001 M201106 1 756152409.35 756152409.35 10597.7 <.0001 M201107 1 254016761.22 254016761.22 3560.11 <.0001 M201108 1 949275985.88 949275985.88 13304.3 <.0001 cdd 1 625708610.24 625708610.24 8769.46 <.0001 hdd 1 96355362.982 96355362.982 1350.44 <.0001 cdd*post 1 401502.00246 401502.00246 5.63 0.0177 hdd*post 1 82138.611557 82138.611557 1.15 0.2833 cdd*Treatment 1 1235207.7697 1235207.7697 17.31 <.0001 hdd*Treatment 1 18200179.127 18200179.127 255.08 <.0001 Other_dum 1 32245659.448 32245659.448 451.93 <.0001 OtherAfter_dum 1 313381.85593 313381.85593 4.39 0.0361 part 1 23650942.801 23650942.801 331.47 <.0001 Source DF Type III SS Mean Square F Value Pr > F M200910 1 28028945.1 28028945.1 392.83 <.0001 M200911 1 18498200.9 18498200.9 259.26 <.0001 M200912 1 25212000.1 25212000.1 353.35 <.0001 M201001 1 23161671.3 23161671.3 324.62 <.0001 M201002 1 6640182.8 6640182.8 93.06 <.0001 M201003 1 16370401.5 16370401.5 229.44 <.0001 M201004 1 14463564.4 14463564.4 202.71 <.0001 M201005 1 16901833.8 16901833.8 236.88 <.0001 M201006 1 4032914.8 4032914.8 56.52 <.0001 M201007 1 9855409.4 9855409.4 138.13 <.0001

66

M201008 1 6769411.0 6769411.0 94.88 <.0001 M201009 1 1871288.9 1871288.9 26.23 <.0001 M201010 1 12801651.3 12801651.3 179.42 <.0001 M201011 1 22981402.2 22981402.2 322.09 <.0001 M201012 1 34546860.9 34546860.9 484.18 <.0001 M201101 1 23564294.7 23564294.7 330.26 <.0001 M201102 1 4705029.2 4705029.2 65.94 <.0001 M201103 1 17927357.6 17927357.6 251.26 <.0001 M201104 1 14295375.3 14295375.3 200.35 <.0001 M201105 1 26394205.1 26394205.1 369.92 <.0001 M201106 1 94177300.1 94177300.1 1319.92 <.0001 M201107 1 266285023.8 266285023.8 3732.05 <.0001 M201108 1 77362376.5 77362376.5 1084.25 <.0001 cdd 1 416623914.2 416623914.2 5839.09 <.0001 hdd 1 37098937.3 37098937.3 519.95 <.0001 cdd*post 1 361306.3 361306.3 5.06 0.0244 hdd*post 1 77096.6 77096.6 1.08 0.2986 cdd*Treatment 1 5729532.4 5729532.4 80.30 <.0001 hdd*Treatment 1 16335086.1 16335086.1 228.94 <.0001 Other_dum 1 14396573.6 14396573.6 201.77 <.0001 OtherAfter_dum 1 290672.4 290672.4 4.07 0.0436 part 1 23650942.8 23650942.8 331.47 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200910 178.5158232 9.00684850 19.82 <.0001 160.8627072 196.1689392 M200911 200.1497797 12.43053692 16.10 <.0001 175.7863510 224.5132083 M200912 306.0749114 16.28259420 18.80 <.0001 274.1615818 337.9882410 M201001 275.3278129 15.28145317 18.02 <.0001 245.3766855 305.2789402 M201002 121.3996719 12.58424057 9.65 <.0001 96.7349894 146.0643545 M201003 182.1267139 12.02384871 15.15 <.0001 158.5603803 205.6930475 M201004 146.1434878 10.26458316 14.24 <.0001 126.0252547 166.2617209 M201005 138.9648571 9.02895711 15.39 <.0001 121.2684090 156.6613053 M201006 75.9389725 10.10077529 7.52 <.0001 56.1417973 95.7361478 M201007 134.9728227 11.48440534 11.75 <.0001 112.4637797 157.4818657 M201008 104.0013759 10.67734217 9.74 <.0001 83.0741492 124.9286026 M201009 51.5997204 10.07572816 5.12 <.0001 31.8516367 71.3478041 M201010 122.1531644 9.11950450 13.39 <.0001 104.2792464 140.0270823 M201011 169.5700591 9.44844691 17.95 <.0001 151.0514253 188.0886930 M201012 256.7514985 11.66831671 22.00 <.0001 233.8819955 279.6210015 M201101 220.5641713 12.13688567 18.17 <.0001 196.7762891 244.3520535 M201102 87.0110569 10.71501535 8.12 <.0001 66.0099921 108.0121218 M201103 158.1222799 9.97549474 15.85 <.0001 138.5706503 177.6739096 M201104 115.5828789 8.16573430 14.15 <.0001 99.5783180 131.5874397 M201105 138.5596199 7.20413607 19.23 <.0001 124.4397587 152.6794810 M201106 102.1549746 2.81180967 36.33 <.0001 96.6439235 107.6660257 M201107 122.5706728 2.00637781 61.09 <.0001 118.6382407 126.5031049 M201108 83.7232183 2.54261456 32.93 <.0001 78.7397804 88.7066562 cdd 2.0058687 0.02625004 76.41 <.0001 1.9544196 2.0573179 hdd 0.5955705 0.02611872 22.80 <.0001 0.5443787 0.6467623 cdd*post -0.0914523 0.04064025 -2.25 0.0244 -0.1711058 -0.0117988 hdd*post 0.0345880 0.03327418 1.04 0.2986 -0.0306283 0.0998043 cdd*Treatment -0.0668492 0.00745995 -8.96 <.0001 -0.0814704 -0.0522279 hdd*Treatment -0.0693160 0.00458113 -15.13 <.0001 -0.0782949 -0.0603372 Other_dum -55.7341108 3.92365689 -14.20 <.0001 -63.4243445 -48.0438770 OtherAfter_dum -10.2657730 5.08615095 -2.02 0.0436 -20.2344555 -0.2970905 part -18.0638340 0.99216801 -18.21 <.0001 -20.0084495 -16.1192185

67

APPENDIX B: UCLA BASED PARTICIPANT SELECTION

Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 8371 53832510810 6430834 102.53 <.0001 Error 185843 11656353250 62722 Corrected Total 194214 65488864060 R-Square Coeff Var Root MSE kwh Mean 0.822010 24.50097 250.4426 1022.174 Source DF Type I SS Mean Square F Value Pr > F cont_acc 8339 51180271721 6137459 97.85 <.0001 M200910 1 52154480 52154480 831.52 <.0001 M200911 1 3851468 3851468 61.41 <.0001 M200912 1 282697458 282697458 4507.19 <.0001 M201001 1 157060954 157060954 2504.10 <.0001 M201002 1 57875625 57875625 922.74 <.0001 M201003 1 21955829 21955829 350.05 <.0001 M201004 1 182115135 182115135 2903.55 <.0001 M201005 1 172132752 172132752 2744.40 <.0001 M201006 1 60957 60957 0.97 0.3242 M201007 1 304888391 304888391 4860.99 <.0001 M201008 1 62992155 62992155 1004.32 <.0001 M201009 1 402448 402448 6.42 0.0113 M201010 1 53864398 53864398 858.79 <.0001 M201011 1 23828988 23828988 379.92 <.0001 M201012 1 116569768 116569768 1858.53 <.0001 M201101 1 127919780 127919780 2039.49 <.0001 M201102 1 25909257 25909257 413.08 <.0001 M201103 1 8204297 8204297 130.81 <.0001 M201104 1 285445834 285445834 4551.00 <.0001 M201105 1 384057329 384057329 6123.22 <.0001 M201106 1 81541028 81541028 1300.05 <.0001 M201107 1 38587964 38587964 615.23 <.0001 M201108 1 105770300 105770300 1686.35 <.0001 cdd 1 74391487 74391487 1186.06 <.0001 hdd 1 15110113 15110113 240.91 <.0001 cdd*post 1 17118 17118 0.27 0.6014 hdd*post 1 2595957 2595957 41.39 <.0001 cdd*Treatment 1 907535 907535 14.47 0.0001 hdd*Treatment 1 127179 127179 2.03 0.1545 Other_dum 1 3665723 3665723 58.44 <.0001 OtherAfter_dum 1 5866 5866 0.09 0.7597 part 1 5531518 5531518 88.19 <.0001 Source DF Type III SS Mean Square F Value Pr > F M200910 1 1975443.39 1975443.39 31.50 <.0001 M200911 1 434615.00 434615.00 6.93 0.0085 M200912 1 491329.32 491329.32 7.83 0.0051 M201001 1 450059.49 450059.49 7.18 0.0074 M201002 1 910.86 910.86 0.01 0.9041 M201003 1 290866.70 290866.70 4.64 0.0313 M201004 1 393636.76 393636.76 6.28 0.0122 M201005 1 1040926.60 1040926.60 16.60 <.0001 M201006 1 205105.53 205105.53 3.27 0.0706 M201007 1 653834.74 653834.74 10.42 0.0012

68

M201008 1 434782.41 434782.41 6.93 0.0085 M201009 1 86703.29 86703.29 1.38 0.2397 M201010 1 912723.16 912723.16 14.55 0.0001 M201011 1 7185976.50 7185976.50 114.57 <.0001 M201012 1 9914676.48 9914676.48 158.07 <.0001 M201101 1 8072999.86 8072999.86 128.71 <.0001 M201102 1 3499131.60 3499131.60 55.79 <.0001 M201103 1 6270097.78 6270097.78 99.97 <.0001 M201104 1 4207913.09 4207913.09 67.09 <.0001 M201105 1 5432138.61 5432138.61 86.61 <.0001 M201106 1 14305635.35 14305635.35 228.08 <.0001 M201107 1 32884312.46 32884312.46 524.29 <.0001 M201108 1 7417845.86 7417845.86 118.27 <.0001 cdd 1 53295164.22 53295164.22 849.71 <.0001 hdd 1 13392722.84 13392722.84 213.53 <.0001 cdd*post 1 135994.34 135994.34 2.17 0.1409 hdd*post 1 2594247.51 2594247.51 41.36 <.0001 cdd*Treatment 1 407429.33 407429.33 6.50 0.0108 hdd*Treatment 1 33458.14 33458.14 0.53 0.4652

69

Dependent Variable: kwh Source DF Type III SS Mean Square F Value Pr > F Other_dum 1 1908890.11 1908890.11 30.43 <.0001 OtherAfter_dum 1 6483.76 6483.76 0.10 0.7478 part 1 5531518.40 5531518.40 88.19 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200910 128.4338702 22.88524268 5.61 <.0001 83.5793267 173.2884138 M200911 81.8822376 31.10610189 2.63 0.0085 20.9150011 142.8494740 M200912 114.2209893 40.81011552 2.80 0.0051 34.2341117 194.2078669 M201001 102.6085133 38.30510689 2.68 0.0074 27.5313945 177.6856322 M201002 3.8114768 31.62824540 0.12 0.9041 -58.1791488 65.8021024 M201003 65.3394088 30.34145801 2.15 0.0313 5.8708565 124.8079610 M201004 65.0181923 25.95346303 2.51 0.0122 14.1500082 115.8863764 M201005 93.0950286 22.85202458 4.07 <.0001 48.3055918 137.8844655 M201006 45.6944989 25.26873393 1.81 0.0706 -3.8316321 95.2206299 M201007 91.8741242 28.45558132 3.23 0.0012 36.1018464 147.6464020 M201008 69.6006276 26.43537158 2.63 0.0085 17.7879139 121.4133412 M201009 29.7007621 25.26143713 1.18 0.2397 -19.8110673 79.2125915 M201010 88.0992140 23.09461594 3.81 0.0001 42.8343037 133.3641243 M201011 252.2237266 23.56412339 10.70 <.0001 206.0385926 298.4088605 M201012 365.9823704 29.10912414 12.57 <.0001 308.9291639 423.0355769 M201101 344.2397578 30.34253080 11.35 <.0001 284.7691029 403.7104127 M201102 199.4591747 26.70435705 7.47 <.0001 147.1192558 251.7990936 M201103 249.7275778 24.97684590 10.00 <.0001 200.7735406 298.6816151 M201104 168.9355084 20.62508795 8.19 <.0001 128.5108156 209.3602012 M201105 167.0210432 17.94708900 9.31 <.0001 131.8451660 202.1969203 M201106 111.4387778 7.37888958 15.10 <.0001 96.9763258 125.9012298 M201107 112.5197081 4.91408269 22.90 <.0001 102.8882203 122.1511960 M201108 67.7315516 6.22816871 10.88 <.0001 55.5244857 79.9386174 cdd 1.8391190 0.06309198 29.15 <.0001 1.7154602 1.9627778 hdd 0.9530868 0.06522383 14.61 <.0001 0.8252497 1.0809240 cdd*post -0.1484516 0.10081677 -1.47 0.1409 -0.3460501 0.0491469 hdd*post -0.5337777 0.08299711 -6.43 <.0001 -0.6964501 -0.3711053 cdd*Treatment -0.0452919 0.01777061 -2.55 0.0108 -0.0801219 -0.0104619 hdd*Treatment -0.0080217 0.01098301 -0.73 0.4652 -0.0295481 0.0135048 Other_dum -51.1663694 9.27475368 -5.52 <.0001 -69.3446710 -32.9880678 OtherAfter_dum 3.7199304 11.56989923 0.32 0.7478 -18.9568031 26.3966639 part -22.0522372 2.34821967 -9.39 <.0001 -26.6546932 -17.4497812

70

APPENDIX C: SMUD SEGMENTATION

Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 8230 50128366320 6090932 74.42 <.0001 Error 182204 14913248438 81849 Corrected Total 190434 65041614758 R-Square Coeff Var Root MSE kwh Mean 0.770712 23.22827 286.0930 1231.658 Source DF Type I SS Mean Square F Value Pr > F cont_acc 8198 45683368214 5572502 68.08 <.0001 M200910 1 48727998 48727998 595.34 <.0001 M200911 1 23277118 23277118 284.39 <.0001 M200912 1 228177079 228177079 2787.77 <.0001 M201001 1 96557762 96557762 1179.70 <.0001 M201002 1 163665819 163665819 1999.60 <.0001 M201003 1 76412245 76412245 933.57 <.0001 M201004 1 320293967 320293967 3913.22 <.0001 M201005 1 236133112 236133112 2884.98 <.0001 M201006 1 11973581 11973581 146.29 <.0001 M201007 1 695725730 695725730 8500.09 <.0001 M201008 1 259866908 259866908 3174.95 <.0001 M201009 1 12280915 12280915 150.04 <.0001 M201010 1 48245827 48245827 589.45 <.0001 M201011 1 64989078 64989078 794.01 <.0001 M201012 1 65086891 65086891 795.21 <.0001 M201101 1 47489282 47489282 580.20 <.0001 M201102 1 142343362 142343362 1739.09 <.0001 M201103 1 88171836 88171836 1077.25 <.0001 M201104 1 588827544 588827544 7194.06 <.0001 M201105 1 744731316 744731316 9098.82 <.0001 M201106 1 130023269 130023269 1588.57 <.0001 M201107 1 45025552 45025552 550.10 <.0001 M201108 1 176940982 176940982 2161.79 <.0001 cdd 1 98085489 98085489 1198.37 <.0001 hdd 1 21058829 21058829 257.29 <.0001 cdd*post 1 85676 85676 1.05 0.3063 hdd*post 1 725663 725663 8.87 0.0029 cdd*Treatment 1 591088 591088 7.22 0.0072 hdd*Treatment 1 159397 159397 1.95 0.1629 Other_dum 1 4252072 4252072 51.95 <.0001 OtherAfter_dum 1 26338 26338 0.32 0.5705 part 1 5046381 5046381 61.65 <.0001 Source DF Type III SS Mean Square F Value Pr > F M200910 1 5423036.69 5423036.69 66.26 <.0001 M200911 1 3698712.31 3698712.31 45.19 <.0001 M200912 1 5157558.78 5157558.78 63.01 <.0001 M201001 1 4608291.09 4608291.09 56.30 <.0001 M201002 1 1316143.30 1316143.30 16.08 <.0001 M201003 1 3209775.80 3209775.80 39.22 <.0001 M201004 1 2773902.52 2773902.52 33.89 <.0001 M201005 1 3135646.11 3135646.11 38.31 <.0001 M201006 1 939434.30 939434.30 11.48 0.0007 M201007 1 2222286.00 2222286.00 27.15 <.0001

71

M201008 1 1537831.42 1537831.42 18.79 <.0001 M201009 1 470344.77 470344.77 5.75 0.0165 M201010 1 2562523.16 2562523.16 31.31 <.0001 M201011 1 1477803.82 1477803.82 18.06 <.0001 M201012 1 2427596.98 2427596.98 29.66 <.0001 M201101 1 1246311.83 1246311.83 15.23 <.0001 M201102 1 883.93 883.93 0.01 0.9172 M201103 1 863576.36 863576.36 10.55 0.0012 M201104 1 1025338.79 1025338.79 12.53 0.0004 M201105 1 3061480.19 3061480.19 37.40 <.0001 M201106 1 14535202.95 14535202.95 177.59 <.0001 M201107 1 50403185.20 50403185.20 615.81 <.0001 M201108 1 15496833.42 15496833.42 189.33 <.0001 cdd 1 62771025.06 62771025.06 766.91 <.0001 hdd 1 4791192.02 4791192.02 58.54 <.0001 cdd*post 1 31385.27 31385.27 0.38 0.5358 hdd*post 1 726601.97 726601.97 8.88 0.0029 cdd*Treatment 1 340103.00 340103.00 4.16 0.0415 hdd*Treatment 1 54425.91 54425.91 0.66 0.4148 Other_dum 1 3003492.45 3003492.45 36.70 <.0001 OtherAfter_dum 1 38241.18 38241.18 0.47 0.4943 part 1 5046381.39 5046381.39 61.65 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200910 201.5944128 24.76648611 8.14 <.0001 153.0526695 250.1361561 M200911 231.8865861 34.49510913 6.72 <.0001 164.2769654 299.4962067 M200912 358.1268494 45.11510389 7.94 <.0001 269.7022832 446.5514156 M201001 317.7254767 42.34374452 7.50 <.0001 234.7327112 400.7182423 M201002 139.8120406 34.86584206 4.01 <.0001 71.4757919 208.1482892 M201003 208.1278357 33.23532972 6.26 <.0001 142.9873538 273.2683177 M201004 164.3321189 28.22825488 5.82 <.0001 109.0053884 219.6588493 M201005 153.7317278 24.83745883 6.19 <.0001 105.0508797 202.4125760 M201006 95.3893346 28.15618346 3.39 0.0007 40.2038624 150.5748067 M201007 168.2559868 32.29071959 5.21 <.0001 104.9669189 231.5450546 M201008 130.0960411 30.01351698 4.33 <.0001 71.2702380 188.9218442 M201009 67.3253160 28.08519300 2.40 0.0165 12.2789835 122.3716484 M201010 140.5639535 25.12159655 5.60 <.0001 91.3262019 189.8017051 M201011 111.1853421 26.16654108 4.25 <.0001 59.8995233 162.4711609 M201012 177.0653744 32.51266137 5.45 <.0001 113.3413058 240.7894430 M201101 131.5263406 33.70594840 3.90 <.0001 65.4634568 197.5892244 M201102 3.0882016 29.71692456 0.10 0.9172 -55.1562872 61.3326904 M201103 89.8213513 27.65262625 3.25 0.0012 35.6228397 144.0198628 M201104 79.2716943 22.39709130 3.54 0.0004 35.3739104 123.1694782 M201105 121.2832157 19.83088743 6.12 <.0001 82.4151323 160.1512990 M201106 100.4446631 7.53743343 13.33 <.0001 85.6714669 115.2178593 M201107 138.6688835 5.58801033 24.82 <.0001 127.7165117 149.6212552 M201108 98.2339865 7.13916566 13.76 <.0001 84.2413860 112.2265871 cdd 2.1060053 0.07604787 27.69 <.0001 1.9569533 2.2550574 hdd 0.5568230 0.07277841 7.65 <.0001 0.4141790 0.6994670 cdd*post -0.0708666 0.11444212 -0.62 0.5358 -0.2951705 0.1534373 hdd*post 0.2774925 0.09313440 2.98 0.0029 0.0949512 0.4600338 cdd*Treatment -0.0419232 0.02056631 -2.04 0.0415 -0.0822327 -0.0016137 hdd*Treatment -0.0103378 0.01267748 -0.82 0.4148 -0.0351854 0.0145098 Other_dum -58.7370398 9.69629804 -6.06 <.0001 -77.7415610 -39.7325186 OtherAfter_dum 9.1012190 13.31499829 0.68 0.4943 -16.9958714 35.1983095 part -21.3671631 2.72122219 -7.85 <.0001 -26.7006961 -16.0336302

72

APPENDIX D: HIGH USE

Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 8274 32342297332 3908907 44.18 <.0001 Error 182225 16122248872 88474 Corrected Total 190499 48464546204 R-Square Coeff Var Root MSE kwh Mean 0.667339 22.29312 297.4465 1334.252 Source DF Type I SS Mean Square F Value Pr > F cont_acc 8242 26914171144 3265490 36.91 <.0001 M200910 1 57573867 57573867 650.74 <.0001 M200911 1 32988726 32988726 372.86 <.0001 M200912 1 258994210 258994210 2927.33 <.0001 M201001 1 117864976 117864976 1332.19 <.0001 M201002 1 184440963 184440963 2084.68 <.0001 M201003 1 86810961 86810961 981.20 <.0001 M201004 1 363445683 363445683 4107.92 <.0001 M201005 1 254955971 254955971 2881.69 <.0001 M201006 1 26309701 26309701 297.37 <.0001 M201007 1 892088269 892088269 10083.0 <.0001 M201008 1 348440617 348440617 3938.32 <.0001 M201009 1 25966969 25966969 293.50 <.0001 M201010 1 46412696 46412696 524.59 <.0001 M201011 1 75640002 75640002 854.94 <.0001 M201012 1 68955105 68955105 779.38 <.0001 M201101 1 46258427 46258427 522.85 <.0001 M201102 1 183135461 183135461 2069.93 <.0001 M201103 1 114630503 114630503 1295.63 <.0001 M201104 1 722807341 722807341 8169.68 <.0001 M201105 1 918802077 918802077 10384.9 <.0001 M201106 1 160193913 160193913 1810.62 <.0001 M201107 1 46631926 46631926 527.07 <.0001 M201108 1 205241951 205241951 2319.79 <.0001 cdd 1 139234290 139234290 1573.72 <.0001 hdd 1 18160845 18160845 205.27 <.0001 cdd*post 1 61918 61918 0.70 0.4028 hdd*post 1 66102 66102 0.75 0.3874 cdd*Treatment 1 3106202 3106202 35.11 <.0001 hdd*Treatment 1 2342562 2342562 26.48 <.0001 Other_dum 1 11132431 11132431 125.83 <.0001 OtherAfter_dum 1 909221 909221 10.28 0.0013 part 1 14522301 14522301 164.14 <.0001 Source DF Type III SS Mean Square F Value Pr > F M200910 1 7165569.36 7165569.36 80.99 <.0001 M200911 1 4921402.69 4921402.69 55.63 <.0001 M200912 1 6630685.09 6630685.09 74.94 <.0001 M201001 1 6170622.69 6170622.69 69.74 <.0001 M201002 1 2107350.68 2107350.68 23.82 <.0001 M201003 1 4605719.40 4605719.40 52.06 <.0001 M201004 1 4159995.25 4159995.25 47.02 <.0001 M201005 1 4692620.12 4692620.12 53.04 <.0001 M201006 1 1216231.65 1216231.65 13.75 0.0002 M201007 1 2324477.39 2324477.39 26.27 <.0001

73

M201008 1 1707509.47 1707509.47 19.30 <.0001 M201009 1 587066.52 587066.52 6.64 0.0100 M201010 1 3501158.84 3501158.84 39.57 <.0001 M201011 1 5317599.73 5317599.73 60.10 <.0001 M201012 1 7672484.65 7672484.65 86.72 <.0001 M201101 1 5171799.85 5171799.85 58.46 <.0001 M201102 1 1081634.43 1081634.43 12.23 0.0005 M201103 1 4022048.40 4022048.40 45.46 <.0001 M201104 1 3357042.87 3357042.87 37.94 <.0001 M201105 1 5976993.79 5976993.79 67.56 <.0001 M201106 1 20121415.96 20121415.96 227.43 <.0001 M201107 1 50374264.77 50374264.77 569.37 <.0001 M201108 1 15380102.29 15380102.29 173.84 <.0001 cdd 1 88613100.03 88613100.03 1001.57 <.0001 hdd 1 6234052.04 6234052.04 70.46 <.0001 cdd*post 1 49093.64 49093.64 0.55 0.4563 hdd*post 1 71079.45 71079.45 0.80 0.3701 cdd*Treatment 1 84.88 84.88 0.00 0.9753 hdd*Treatment 1 1540585.13 1540585.13 17.41 <.0001 Other_dum 1 2151828.34 2151828.34 24.32 <.0001 OtherAfter_dum 1 882294.58 882294.58 9.97 0.0016 part 1 14522301.41 14522301.41 164.14 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200910 233.9015897 25.99062075 9.00 <.0001 182.9605707 284.8426086 M200911 267.1020199 35.81307005 7.46 <.0001 196.9092262 337.2948136 M200912 404.1794674 46.68785021 8.66 <.0001 312.6723546 495.6865801 M201001 366.2238448 43.85215625 8.35 <.0001 280.2746270 452.1730626 M201002 176.5662417 36.17829519 4.88 <.0001 105.6576151 247.4748683 M201003 249.0952475 34.52435194 7.22 <.0001 181.4283116 316.7621833 M201004 202.7479720 29.56779458 6.86 <.0001 144.7957746 260.7001694 M201005 190.0022167 26.08914218 7.28 <.0001 138.8680980 241.1363354 M201006 108.3915115 29.23451902 3.71 0.0002 51.0925265 165.6904965 M201007 170.5161501 33.26685053 5.13 <.0001 105.3138881 235.7184121 M201008 136.2068929 31.00461602 4.39 <.0001 75.4385585 196.9752273 M201009 75.0366998 29.12989357 2.58 0.0100 17.9427783 132.1306213 M201010 165.6817204 26.33768018 6.29 <.0001 114.0604730 217.3029679 M201011 211.8980273 27.33240974 7.75 <.0001 158.3271327 265.4689218 M201012 312.8202988 33.59199085 9.31 <.0001 246.9807692 378.6598283 M201101 266.4252030 34.84683041 7.65 <.0001 198.1262168 334.7241893 M201102 108.1000607 30.91679868 3.50 0.0005 47.5038463 168.6962752 M201103 194.1291530 28.79227254 6.74 <.0001 137.6969609 250.5613450 M201104 145.6127315 23.63903459 6.16 <.0001 99.2807673 191.9446956 M201105 172.6048746 21.00005757 8.22 <.0001 131.4452447 213.7645044 M201106 120.2956922 7.97681715 15.08 <.0001 104.6613140 135.9300704 M201107 139.1854656 5.83308570 23.86 <.0001 127.7527518 150.6181795 M201108 98.0135889 7.43387939 13.18 <.0001 83.4433562 112.5838215 cdd 2.4095819 0.07613802 31.65 <.0001 2.2603531 2.5588107 hdd 0.6263968 0.07462308 8.39 <.0001 0.4801373 0.7726564 cdd*post -0.0878501 0.11793384 -0.74 0.4563 -0.3189977 0.1432975 hdd*post 0.0850808 0.09492237 0.90 0.3701 -0.1009648 0.2711265 cdd*Treatment 0.0006587 0.02126540 0.03 0.9753 -0.0410210 0.0423384 hdd*Treatment -0.0546411 0.01309439 -4.17 <.0001 -0.0803058 -0.0289764 Other_dum -62.9514343 12.76470109 -4.93 <.0001 -87.9699549 -37.9329137 OtherAfter_dum -48.3531583 15.31182204 -3.16 0.0016 -78.3639774 -18.3423392 part -36.2210090 2.82716823 -12.81 <.0001 -41.7621937 -30.6798243

74

APPENDIX E: E-REPORTS

Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 10901 41465859537 3803858 99.66 <.0001 Error 238624 9107618089 38167 Corrected Total 249525 50573477626 R-Square Coeff Var Root MSE kwh Mean 0.819913 24.30589 195.3644 803.7737 Source DF Type I SS Mean Square F Value Pr > F cont_acc 10869 38106049350 3505939 91.86 <.0001 M200910 1 41630722 41630722 1090.75 <.0001 M200911 1 46814596 46814596 1226.57 <.0001 M200912 1 47449369 47449369 1243.20 <.0001 M201001 1 11980712 11980712 313.90 <.0001 M201002 1 159557721 159557721 4180.49 <.0001 M201003 1 98353569 98353569 2576.91 <.0001 M201004 1 267978636 267978636 7021.17 <.0001 M201005 1 198690002 198690002 5205.77 <.0001 M201006 1 7105647 7105647 186.17 <.0001 M201007 1 512687903 512687903 13432.7 <.0001 M201008 1 183127476 183127476 4798.03 <.0001 M201009 1 14947847 14947847 391.64 <.0001 M201010 1 29900526 29900526 783.41 <.0001 M201011 1 68352438 68352438 1790.87 <.0001 M201012 1 8112504 8112504 212.55 <.0001 M201101 1 2644812 2644812 69.30 <.0001 M201102 1 149795716 149795716 3924.72 <.0001 M201103 1 108707474 108707474 2848.19 <.0001 M201104 1 472983978 472983978 12392.4 <.0001 M201105 1 593773239 593773239 15557.1 <.0001 M201106 1 107452076 107452076 2815.30 <.0001 M201107 1 24389995 24389995 639.03 <.0001 M201108 1 101742272 101742272 2665.70 <.0001 cdd 1 70844959 70844959 1856.17 <.0001 hdd 1 7447347 7447347 195.12 <.0001 cdd*post 1 75447 75447 1.98 0.1597 hdd*post 1 123047 123047 3.22 0.0726 cdd*Treatment 1 16271784 16271784 426.33 <.0001 hdd*Treatment 1 58852 58852 1.54 0.2143 Other_dum 1 3623731 3623731 94.94 <.0001 OtherAfter_dum 1 255747 255747 6.70 0.0096 part 1 2930046 2930046 76.77 <.0001 Source DF Type III SS Mean Square F Value Pr > F M200910 1 1496216.30 1496216.30 39.20 <.0001 M200911 1 1135783.85 1135783.85 29.76 <.0001 M200912 1 1953149.11 1953149.11 51.17 <.0001 M201001 1 1752405.47 1752405.47 45.91 <.0001 M201002 1 310612.74 310612.74 8.14 0.0043 M201003 1 985793.36 985793.36 25.83 <.0001 M201004 1 671836.57 671836.57 17.60 <.0001 M201005 1 638263.11 638263.11 16.72 <.0001 M201006 1 79047.48 79047.48 2.07 0.1501

75

M201007 1 769453.61 769453.61 20.16 <.0001 M201008 1 366241.71 366241.71 9.60 0.0020 M201009 1 23302.08 23302.08 0.61 0.4346 M201010 1 659634.76 659634.76 17.28 <.0001 M201011 1 959325.35 959325.35 25.13 <.0001 M201012 1 1976355.88 1976355.88 51.78 <.0001 M201101 1 1145031.97 1145031.97 30.00 <.0001 M201102 1 39770.87 39770.87 1.04 0.3074 M201103 1 772726.69 772726.69 20.25 <.0001 M201104 1 447629.31 447629.31 11.73 0.0006 M201105 1 1129015.76 1129015.76 29.58 <.0001 M201106 1 6208635.39 6208635.39 162.67 <.0001 M201107 1 29440435.57 29440435.57 771.35 <.0001 M201108 1 9771880.91 9771880.91 256.03 <.0001 cdd 1 40522572.79 40522572.79 1061.71 <.0001 hdd 1 1995530.79 1995530.79 52.28 <.0001 cdd*post 1 47752.30 47752.30 1.25 0.2633 hdd*post 1 122305.80 122305.80 3.20 0.0734 cdd*Treatment 1 4783822.02 4783822.02 125.34 <.0001 hdd*Treatment 1 12975.74 12975.74 0.34 0.5598 Other_dum 1 1463138.05 1463138.05 38.33 <.0001 OtherAfter_dum 1 235902.53 235902.53 6.18 0.0129 part 1 2930046.21 2930046.21 76.77 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200910 92.6418240 14.79637234 6.26 <.0001 63.6413201 121.6423280 M200911 111.3739479 20.41649163 5.46 <.0001 71.3581567 151.3897392 M200912 191.5524720 26.77721502 7.15 <.0001 139.0698287 244.0351152 M201001 170.2245952 25.12177691 6.78 <.0001 120.9865675 219.4626229 M201002 58.8841404 20.64115768 2.85 0.0043 18.4280095 99.3402713 M201003 100.2454189 19.72499652 5.08 <.0001 61.5849400 138.9058977 M201004 70.9094605 16.90120639 4.20 <.0001 37.7835367 104.0353843 M201005 60.8064163 14.86944921 4.09 <.0001 31.6626836 89.9501491 M201006 23.8477648 16.57101163 1.44 0.1501 -8.6309859 56.3265155 M201007 84.4788782 18.81491508 4.49 <.0001 47.6021352 121.3556211 M201008 54.1899111 17.49362780 3.10 0.0020 19.9028568 88.4769655 M201009 12.8912176 16.49840394 0.78 0.4346 -19.4452239 45.2276592 M201010 62.2731111 14.97939044 4.16 <.0001 32.9138965 91.6323258 M201011 78.0299148 15.56408224 5.01 <.0001 47.5247194 108.5351102 M201012 137.3135642 19.08210187 7.20 <.0001 99.9131421 174.7139864 M201101 109.0973945 19.91823801 5.48 <.0001 70.0581673 148.1366216 M201102 17.9988491 17.63224199 1.02 0.3074 -16.5598855 52.5575837 M201103 73.5592361 16.34818707 4.50 <.0001 41.5172157 105.6012565 M201104 46.0374285 13.44302924 3.42 0.0006 19.6894417 72.3854153 M201105 64.9983669 11.95082228 5.44 <.0001 41.5750668 88.4216669 M201106 58.7775534 4.60849129 12.75 <.0001 49.7450306 67.8100762 M201107 91.4040158 3.29108051 27.77 <.0001 84.9535839 97.8544478 M201108 66.2376563 4.13962708 16.00 <.0001 58.1240952 74.3512175 cdd 1.3962151 0.04284984 32.58 <.0001 1.3122305 1.4801997 hdd 0.3102214 0.04290302 7.23 <.0001 0.2261326 0.3943102 cdd*post -0.0741108 0.06625672 -1.12 0.2633 -0.2039723 0.0557506 hdd*post 0.0974125 0.05441726 1.79 0.0734 -0.0092439 0.2040689 cdd*Treatment 0.1336182 0.01193503 11.20 <.0001 0.1102259 0.1570105 hdd*Treatment -0.0042913 0.00735977 -0.58 0.5598 -0.0187162 0.0101337 Other_dum -42.6483633 6.88818910 -6.19 <.0001 -56.1490343 -29.1476923 OtherAfter_dum -23.8752151 9.60342917 -2.49 0.0129 -42.6976859 -5.0527443 part -14.0008789 1.59795082 -8.76 <.0001 -17.1328209 -10.8689370

76

APPENDIX F: SEASONAL BURST

Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 19888 119144069216 5990751.6702 75.41 <.0001 Error 441228 35050021153 79437.43632 Corrected Total 461116 154194090369 R-Square Coeff Var Root MSE kwh Mean 0.772689 23.49781 281.8465 1199.458 Source DF Type I SS Mean Square F Value Pr > F cont_acc 19856 109596967881 5519589.438 69.48 <.0001 M200910 1 144275751.9 144275751.9 1816.22 <.0001 M200911 1 51137655.895 51137655.895 643.75 <.0001 M200912 1 593205355.82 593205355.82 7467.58 <.0001 M201001 1 279024256 279024256 3512.50 <.0001 M201002 1 327100453.46 327100453.46 4117.71 <.0001 M201003 1 153180278.2 153180278.2 1928.31 <.0001 M201004 1 716477594.86 716477594.86 9019.39 <.0001 M201005 1 564937143.27 564937143.27 7111.72 <.0001 M201006 1 12571266.912 12571266.912 158.25 <.0001 M201007 1 1368617064.3 1368617064.3 17228.9 <.0001 M201008 1 433023844.09 433023844.09 5451.13 <.0001 M201009 1 7613811.107 7613811.107 95.85 <.0001 M201010 1 138549826.42 138549826.42 1744.14 <.0001 M201011 1 128548556.95 128548556.95 1618.24 <.0001 M201012 1 210399913.1 210399913.1 2648.62 <.0001 M201101 1 186077411.04 186077411.04 2342.44 <.0001 M201102 1 235656631.45 235656631.45 2966.57 <.0001 M201103 1 128425725.79 128425725.79 1616.69 <.0001 M201104 1 1207801596.7 1207801596.7 15204.4 <.0001 M201105 1 1563410708.5 1563410708.5 19681.0 <.0001 M201106 1 288778089.72 288778089.72 3635.29 <.0001 M201107 1 104429991 104429991 1314.62 <.0001 M201108 1 383359028.09 383359028.09 4825.92 <.0001 cdd 1 261148835.92 261148835.92 3287.48 <.0001 hdd 1 43850214.346 43850214.346 552.01 <.0001 cdd*post 1 274400.14432 274400.14432 3.45 0.0631 hdd*post 1 828.87780762 828.87780762 0.01 0.9186 cdd*Treatment 1 2955740.2518 2955740.2518 37.21 <.0001 hdd*Treatment 1 9474.1959763 9474.1959763 0.12 0.7298 Other_dum 1 10106369.783 10106369.783 127.22 <.0001 OtherAfter_dum 1 10181.615005 10181.615005 0.13 0.7203 part 1 2143336.2237 2143336.2237 26.98 <.0001 Source DF Type III SS Mean Square F Value Pr > F M200910 1 12101167.2 12101167.2 152.34 <.0001 M200911 1 7630659.2 7630659.2 96.06 <.0001 M200912 1 10024972.0 10024972.0 126.20 <.0001 M201001 1 9263632.9 9263632.9 116.62 <.0001 M201002 1 2755264.1 2755264.1 34.68 <.0001 M201003 1 6827446.5 6827446.5 85.95 <.0001 M201004 1 6367024.7 6367024.7 80.15 <.0001 M201005 1 7636136.4 7636136.4 96.13 <.0001 M201006 1 1625141.9 1625141.9 20.46 <.0001 M201007 1 3557341.8 3557341.8 44.78 <.0001

77

M201008 1 2541045.4 2541045.4 31.99 <.0001 M201009 1 713708.0 713708.0 8.98 0.0027 M201010 1 5178596.2 5178596.2 65.19 <.0001 M201011 1 10394945.1 10394945.1 130.86 <.0001 M201012 1 15076650.1 15076650.1 189.79 <.0001 M201101 1 10479079.4 10479079.4 131.92 <.0001 M201102 1 2358874.4 2358874.4 29.69 <.0001 M201103 1 8162973.5 8162973.5 102.76 <.0001 M201104 1 6935740.5 6935740.5 87.31 <.0001 M201105 1 12729759.4 12729759.4 160.25 <.0001 M201106 1 41877661.4 41877661.4 527.18 <.0001 M201107 1 105472254.7 105472254.7 1327.74 <.0001 M201108 1 29436989.0 29436989.0 370.57 <.0001 cdd 1 174178205.7 174178205.7 2192.65 <.0001 hdd 1 16427429.7 16427429.7 206.80 <.0001 cdd*post 1 259248.0 259248.0 3.26 0.0708 hdd*post 1 901.7 901.7 0.01 0.9152 cdd*Treatment 1 361559.6 361559.6 4.55 0.0329 hdd*Treatment 1 39809.8 39809.8 0.50 0.4790 Other_dum 1 5700551.7 5700551.7 71.76 <.0001 OtherAfter_dum 1 9893.4 9893.4 0.12 0.7242 part 1 2143336.2 2143336.2 26.98 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200910 196.2091816 15.89711461 12.34 <.0001 165.0513240 227.3670392 M200911 215.3691335 21.97429965 9.80 <.0001 172.3001795 258.4380876 M200912 323.4678771 28.79401213 11.23 <.0001 267.0324955 379.9032586 M201001 291.8126805 27.02253584 10.80 <.0001 238.8493382 344.7760228 M201002 131.0636655 22.25426756 5.89 <.0001 87.4459830 174.6813481 M201003 197.1023211 21.26058432 9.27 <.0001 155.4322272 238.7724150 M201004 162.2745080 18.12569405 8.95 <.0001 126.7487030 197.8003130 M201005 156.1679244 15.92823334 9.80 <.0001 124.9490751 187.3867737 M201006 80.6021393 17.82023967 4.52 <.0001 45.6750155 115.5292630 M201007 135.5897424 20.26173578 6.69 <.0001 95.8773611 175.3021238 M201008 106.5552637 18.84001765 5.66 <.0001 69.6294063 143.4811210 M201009 53.3075805 17.78448578 3.00 0.0027 18.4505333 88.1646277 M201010 130.0103272 16.10217103 8.07 <.0001 98.4505653 161.5700891 M201011 190.8979766 16.68794403 11.44 <.0001 158.1901176 223.6058356 M201012 284.4380766 20.64657011 13.78 <.0001 243.9714318 324.9047214 M201101 246.5153184 21.46322389 11.49 <.0001 204.4480571 288.5825796 M201102 103.1692326 18.93260622 5.45 <.0001 66.0619045 140.2765607 M201103 178.8226581 17.64050297 10.14 <.0001 144.2478128 213.3975035 M201104 134.7632430 14.42240361 9.34 <.0001 106.4957738 163.0307122 M201105 160.5452169 12.68235438 12.66 <.0001 135.6881909 185.4022429 M201106 114.0977380 4.96933682 22.96 <.0001 104.3579901 123.8374859 M201107 129.0782440 3.54239139 36.44 <.0001 122.1352655 136.0212226 M201108 86.4945370 4.49318805 19.25 <.0001 77.6880261 95.3010479 cdd 2.1665717 0.04626886 46.83 <.0001 2.0758862 2.2572573 hdd 0.6644732 0.04620673 14.38 <.0001 0.5739094 0.7550370 cdd*post -0.1296354 0.07175934 -1.81 0.0708 -0.2702815 0.0110107 hdd*post 0.0062770 0.05891607 0.11 0.9152 -0.1091967 0.1217507 cdd*Treatment -0.0323733 0.01517431 -2.13 0.0329 -0.0621144 -0.0026321 hdd*Treatment -0.0064014 0.00904256 -0.71 0.4790 -0.0241245 0.0113218 Other_dum -58.1770218 6.86761042 -8.47 <.0001 -71.6373278 -44.7167158 OtherAfter_dum 3.1414474 8.90163456 0.35 0.7242 -14.3054836 20.5883784 part -14.8005985 2.84935614 -5.19 <.0001 -20.3852492 -9.2159477

78

APPENDIX G: CHANNEL EFFECTIVENESS

Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 45539 240411242030 5279238.4995 100.60 <.0001 Error 1E6 52699216331 52478.753089 Corrected Total 1.05E6 293110458361 R-Square Coeff Var Root MSE kwh Mean 0.820207 24.86560 229.0824 921.2826 Source DF Type I SS Mean Square F Value Pr > F cont_acc 45506 224823102275 4940515.5864 94.14 <.0001 M200910 1 210145553.48 210145553.48 4004.39 <.0001 M200911 1 141955404.14 141955404.14 2705.01 <.0001 M200912 1 496701639.97 496701639.97 9464.81 <.0001 M201001 1 193208451.66 193208451.66 3681.65 <.0001 M201002 1 627243659.32 627243659.32 11952.3 <.0001 M201003 1 340146877.8 340146877.8 6481.61 <.0001 M201004 1 1178640200.8 1178640200.8 22459.4 <.0001 M201005 1 906763179.56 906763179.56 17278.7 <.0001 M201006 1 29828591.374 29828591.374 568.39 <.0001 M201007 1 2373674126.4 2373674126.4 45231.1 <.0001 M201008 1 804010548.26 804010548.26 15320.7 <.0001 M201009 1 44126160.344 44126160.344 840.84 <.0001 M201010 1 166020559 166020559 3163.58 <.0001 M201011 1 257019910.96 257019910.96 4897.60 <.0001 M201012 1 139638307.26 139638307.26 2660.85 <.0001 M201101 1 93385373.366 93385373.366 1779.49 <.0001 M201102 1 544931640.96 544931640.96 10383.9 <.0001 M201103 1 353923754.01 353923754.01 6744.13 <.0001 M201104 1 2093164974.7 2093164974.7 39886.0 <.0001 M201105 1 2685322974.2 2685322974.2 51169.7 <.0001 M201106 1 483285960.2 483285960.2 9209.17 <.0001 M201107 1 144467122.68 144467122.68 2752.87 <.0001 M201108 1 539912592.11 539912592.11 10288.2 <.0001 cdd 1 351666777.37 351666777.37 6701.13 <.0001 hdd 1 45729081.324 45729081.324 871.38 <.0001 cdd*post 1 1731.2559357 1731.2559357 0.03 0.8559 hdd*post 1 9017.2979355 9017.2979355 0.17 0.6785 cdd*Treatment 1 17488164.735 17488164.735 333.24 <.0001 hdd*Treatment 1 181379204.12 181379204.12 3456.24 <.0001 Other_dum 1 28168164.673 28168164.673 536.75 <.0001 OtherAfter_dum 1 976751.24994 976751.24994 18.61 <.0001 paper 1 109076683.48 109076683.48 2078.49 <.0001 email 1 6126616.6017 6126616.6017 116.74 <.0001 Source DF Type III SS Mean Square F Value Pr > F M200910 1 11771926.3 11771926.3 224.32 <.0001 M200911 1 7217801.3 7217801.3 137.54 <.0001 M200912 1 10882507.7 10882507.7 207.37 <.0001 M201001 1 9873870.4 9873870.4 188.15 <.0001 M201002 1 2063143.2 2063143.2 39.31 <.0001 M201003 1 6311162.0 6311162.0 120.26 <.0001 M201004 1 5118529.2 5118529.2 97.54 <.0001 M201005 1 6195916.4 6195916.4 118.07 <.0001 M201006 1 1860544.3 1860544.3 35.45 <.0001

79

M201007 1 6672483.3 6672483.3 127.15 <.0001 M201008 1 4043984.0 4043984.0 77.06 <.0001 M201009 1 966837.4 966837.4 18.42 <.0001 M201010 1 6542373.3 6542373.3 124.67 <.0001 M201011 1 11169856.5 11169856.5 212.85 <.0001 M201012 1 18126956.3 18126956.3 345.42 <.0001 M201101 1 12052551.9 12052551.9 229.67 <.0001 M201102 1 2058239.4 2058239.4 39.22 <.0001 M201103 1 8996141.9 8996141.9 171.42 <.0001 M201104 1 6289024.4 6289024.4 119.84 <.0001 M201105 1 12556515.8 12556515.8 239.27 <.0001 M201106 1 48958765.8 48958765.8 932.93 <.0001 M201107 1 155172201.6 155172201.6 2956.86 <.0001 M201108 1 45487393.8 45487393.8 866.78 <.0001 cdd 1 165856221.1 165856221.1 3160.45 <.0001 hdd 1 9246358.7 9246358.7 176.19 <.0001 cdd*post 1 1643.8 1643.8 0.03 0.8595 hdd*post 1 10997.5 10997.5 0.21 0.6471 cdd*Treatment 1 210997530.8 210997530.8 4020.63 <.0001 hdd*Treatment 1 190872474.6 190872474.6 3637.14 <.0001 Other_dum 1 11664395.5 11664395.5 222.27 <.0001 OtherAfter_dum 1 581703.7 581703.7 11.08 0.0009 paper 1 115194024.5 115194024.5 2195.06 <.0001 email 1 6126616.6 6126616.6 116.74 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200910 126.7163751 8.46059152 14.98 <.0001 110.1339005 143.2988498 M200911 137.1446182 11.69413387 11.73 <.0001 114.2245093 160.0647270 M200912 220.9665490 15.34453722 14.40 <.0001 190.8917724 251.0413256 M201001 197.4658141 14.39593780 13.72 <.0001 169.2502605 225.6813677 M201002 74.1975979 11.83360065 6.27 <.0001 51.0041389 97.3910569 M201003 123.9910254 11.30647801 10.97 <.0001 101.8307090 146.1513418 M201004 95.4099064 9.66078975 9.88 <.0001 76.4750836 114.3447292 M201005 92.2403404 8.48906536 10.87 <.0001 75.6020580 108.8786229 M201006 56.5286705 9.49380951 5.95 <.0001 37.9211233 75.1362176 M201007 121.7694158 10.79906680 11.28 <.0001 100.6036083 142.9352234 M201008 88.0550881 10.03093682 8.78 <.0001 68.3947895 107.7153867 M201009 40.6121110 9.46173113 4.29 <.0001 22.0674364 59.1567856 M201010 95.6551424 8.56707199 11.17 <.0001 78.8639696 112.4463152 M201011 129.7866775 8.89606753 14.59 <.0001 112.3506845 147.2226704 M201012 203.7817625 10.96464623 18.59 <.0001 182.2914249 225.2721002 M201101 173.1366165 11.42460449 15.15 <.0001 150.7447761 195.5284568 M201102 63.2014881 10.09185766 6.26 <.0001 43.4217867 82.9811895 M201103 122.7237060 9.37329030 13.09 <.0001 104.3523724 141.0950395 M201104 84.0496367 7.67778512 10.95 <.0001 69.0014363 99.0978372 M201105 105.0264627 6.78978338 15.47 <.0001 91.7187158 118.3342096 M201106 80.8505802 2.64703488 30.54 <.0001 75.6624809 86.0386795 M201107 102.8251700 1.89096821 54.38 <.0001 99.1189359 106.5314040 M201108 70.1286559 2.38199992 29.44 <.0001 65.4600162 74.7972956 cdd 1.3869206 0.02467047 56.22 <.0001 1.3385673 1.4352739 hdd 0.3266775 0.02461080 13.27 <.0001 0.2784412 0.3749139 cdd*post 0.0067438 0.03810465 0.18 0.8595 -0.0679400 0.0814277 hdd*post 0.0143378 0.03132035 0.46 0.6471 -0.0470491 0.0757246 cdd*Treatment 0.4349335 0.00685924 63.41 <.0001 0.4214896 0.4483774 hdd*Treatment 0.2541056 0.00421342 60.31 <.0001 0.2458475 0.2623638 Other_dum -56.8510451 3.81328344 -14.91 <.0001 -64.3249523 -49.3771379 OtherAfter_dum -17.2523431 5.18189902 -3.33 0.0009 -27.4086908 -7.0959954 paper -47.4361717 1.01247990 -46.85 <.0001 -49.4205983 -45.4517452 email -15.0116926 1.38934808 -10.80 <.0001 -17.7347681 -12.2886171

80

APPENDIX H: OVERALL SAVING OF WAVE 1

Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 83574 850411461077 10175550.543 176.05 <.0001 Error 3.92E6 226600561448 57797.861395 Corrected Total 4E6 1.077012E12 R-Square Coeff Var Root MSE kwh Mean 0.789603 26.09498 240.4119 921.2955 Source DF Type I SS Mean Square F Value Pr > F cont_acc 83512 780111781884 9341313.6062 161.62 <.0001 M200704 1 2319721482 2319721482 40135.1 <.0001 M200705 1 663360401.33 663360401.33 11477.2 <.0001 M200706 1 529592063.97 529592063.97 9162.83 <.0001 M200707 1 4630798576.8 4630798576.8 80120.6 <.0001 M200708 1 5427031663.5 5427031663.5 93896.8 <.0001 M200709 1 36892069.53 36892069.53 638.29 <.0001 M200710 1 1540925594 1540925594 26660.6 <.0001 M200711 1 340376609.68 340376609.68 5889.09 <.0001 M200712 1 2025616037 2025616037 35046.6 <.0001 M200801 1 2134057597.3 2134057597.3 36922.8 <.0001 M200802 1 62429615.113 62429615.113 1080.14 <.0001 M200803 1 707206319.63 707206319.63 12235.9 <.0001 M200804 1 2108239752.8 2108239752.8 36476.1 <.0001 M200805 1 315165995.1 315165995.1 5452.90 <.0001 M200806 1 694212589.32 694212589.32 12011.0 <.0001 M200807 1 5444117061.5 5444117061.5 94192.4 <.0001 M200808 1 5535838781.8 5535838781.8 95779.3 <.0001 M200809 1 192457078.19 192457078.19 3329.83 <.0001 M200810 1 988225575.71 988225575.71 17098.0 <.0001 M200811 1 426783228.25 426783228.25 7384.07 <.0001 M200812 1 1840750633.2 1840750633.2 31848.1 <.0001 M200901 1 1130758412.7 1130758412.7 19564.0 <.0001 M200902 1 337735434.39 337735434.39 5843.39 <.0001 M200903 1 415679969.5 415679969.5 7191.96 <.0001 M200904 1 1718765931 1718765931 29737.5 <.0001 M200905 1 352803332.42 352803332.42 6104.09 <.0001 M200906 1 33212939.832 33212939.832 574.64 <.0001 M200907 1 4843449459.1 4843449459.1 83799.8 <.0001 M200908 1 3566946760.5 3566946760.5 61714.2 <.0001 M200909 1 500005900.69 500005900.69 8650.94 <.0001 M200910 1 469450935.32 469450935.32 8122.29 <.0001

81

Dependent Variable: kwh Source DF Type I SS Mean Square F Value Pr > F M200911 1 42954310.225 42954310.225 743.18 <.0001 M200912 1 1588454570.6 1588454570.6 27482.9 <.0001 M201001 1 777176991.74 777176991.74 13446.5 <.0001 M201002 1 482438045.12 482438045.12 8346.99 <.0001 M201003 1 232675624.1 232675624.1 4025.68 <.0001 M201004 1 1435350933.3 1435350933.3 24834.0 <.0001 M201005 1 1333034998.5 1333034998.5 23063.7 <.0001 M201006 1 969487.19693 969487.19693 16.77 <.0001 M201007 1 2511383699.1 2511383699.1 43451.2 <.0001 M201008 1 712584732.48 712584732.48 12328.9 <.0001 M201009 1 10745045.768 10745045.768 185.91 <.0001 M201010 1 478196442.35 478196442.35 8273.60 <.0001 M201011 1 126831848.39 126831848.39 2194.40 <.0001 M201012 1 880791359.39 880791359.39 15239.2 <.0001 M201101 1 834461782.41 834461782.41 14437.6 <.0001 M201102 1 155497153.89 155497153.89 2690.36 <.0001 M201103 1 34233008.114 34233008.114 592.29 <.0001 M201104 1 1767536800.1 1767536800.1 30581.4 <.0001 M201105 1 2880891420.3 2880891420.3 49844.3 <.0001 M201106 1 654560868.23 654560868.23 11325.0 <.0001 M201107 1 191423859.56 191423859.56 3311.95 <.0001 M201108 1 317756533.79 317756533.79 5497.72 <.0001 cdd 1 983253382.81 983253382.81 17011.9 <.0001 hdd 1 249776223.93 249776223.93 4321.55 <.0001 cdd*post 1 6821226.7055 6821226.7055 118.02 <.0001 hdd*post 1 329619.51208 329619.51208 5.70 0.0169 cdd*Treatment 1 21236899.635 21236899.635 367.43 <.0001 hdd*Treatment 1 1805927.0177 1805927.0177 31.25 <.0001 Other_dum 1 173776326.93 173776326.93 3006.62 <.0001 OtherAfter_dum 1 4786483.3513 4786483.3513 82.81 <.0001 part 1 75335787.641 75335787.641 1303.44 <.0001 Source DF Type III SS Mean Square F Value Pr > F M200704 1 10026436.4 10026436.4 173.47 <.0001 M200705 1 11970743.1 11970743.1 207.11 <.0001 M200706 1 5430478.5 5430478.5 93.96 <.0001 M200707 1 7733262.7 7733262.7 133.80 <.0001 M200708 1 4318555.6 4318555.6 74.72 <.0001 M200709 1 11680842.4 11680842.4 202.10 <.0001 M200710 1 49898462.8 49898462.8 863.33 <.0001 M200711 1 21359594.8 21359594.8 369.56 <.0001 M200712 1 19863126.4 19863126.4 343.67 <.0001 M200801 1 17045276.5 17045276.5 294.91 <.0001 M200802 1 8520055.3 8520055.3 147.41 <.0001 M200803 1 16440271.7 16440271.7 284.44 <.0001

82

Dependent Variable: kwh Source DF Type III SS Mean Square F Value Pr > F M200804 1 5429974.8 5429974.8 93.95 <.0001 M200805 1 86763822.8 86763822.8 1501.16 <.0001 M200806 1 126727606.3 126727606.3 2192.60 <.0001 M200807 1 196465047.8 196465047.8 3399.18 <.0001 M200808 1 150844986.3 150844986.3 2609.87 <.0001 M200809 1 53235861.6 53235861.6 921.07 <.0001 M200810 1 63563813.4 63563813.4 1099.76 <.0001 M200811 1 43577764.8 43577764.8 753.97 <.0001 M200812 1 42992619.4 42992619.4 743.84 <.0001 M200901 1 33621056.5 33621056.5 581.70 <.0001 M200902 1 3365165.8 3365165.8 58.22 <.0001 M200903 1 20614569.2 20614569.2 356.67 <.0001 M200904 1 10533612.3 10533612.3 182.25 <.0001 M200905 1 84913993.3 84913993.3 1469.15 <.0001 M200906 1 148041397.9 148041397.9 2561.36 <.0001 M200907 1 324084599.9 324084599.9 5607.21 <.0001 M200908 1 155964490.5 155964490.5 2698.45 <.0001 M200909 1 30804359.7 30804359.7 532.97 <.0001 M200910 1 60873752.9 60873752.9 1053.22 <.0001 M200911 1 18213849.0 18213849.0 315.13 <.0001 M200912 1 33155101.8 33155101.8 573.64 <.0001 M201001 1 26702950.7 26702950.7 462.01 <.0001 M201002 1 611304.7 611304.7 10.58 0.0011 M201003 1 11309014.0 11309014.0 195.66 <.0001 M201004 1 7920496.8 7920496.8 137.04 <.0001 M201005 1 22529707.8 22529707.8 389.80 <.0001 M201006 1 58908736.6 58908736.6 1019.22 <.0001 M201007 1 344116950.0 344116950.0 5953.80 <.0001 M201008 1 169022779.5 169022779.5 2924.38 <.0001 M201009 1 14802963.1 14802963.1 256.12 <.0001 M201010 1 30100331.8 30100331.8 520.79 <.0001 M201011 1 18045254.9 18045254.9 312.21 <.0001 M201012 1 34830000.3 34830000.3 602.62 <.0001 M201101 1 19123474.2 19123474.2 330.87 <.0001 M201102 1 125076.3 125076.3 2.16 0.1413 M201103 1 11043132.9 11043132.9 191.06 <.0001 M201104 1 8137876.1 8137876.1 140.80 <.0001 M201105 1 25064966.6 25064966.6 433.67 <.0001 M201106 1 82435115.3 82435115.3 1426.27 <.0001 M201107 1 239409383.3 239409383.3 4142.18 <.0001 M201108 1 80239791.2 80239791.2 1388.28 <.0001 cdd 1 330973458.7 330973458.7 5726.40 <.0001 hdd 1 43072904.5 43072904.5 745.23 <.0001 cdd*post 1 6684121.4 6684121.4 115.65 <.0001 hdd*post 1 379712.8 379712.8 6.57 0.0104 cdd*Treatment 1 11101255.5 11101255.5 192.07 <.0001 hdd*Treatment 1 1047810.1 1047810.1 18.13 <.0001

83

Dependent Variable: kwh Source DF Type III SS Mean Square F Value Pr > F Other_dum 1 17946619.1 17946619.1 310.51 <.0001 OtherAfter_dum 1 4699249.8 4699249.8 81.30 <.0001 part 1 75335787.6 75335787.6 1303.44 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200704 59.8521337 4.54425006 13.17 <.0001 50.9455645 68.7587029 M200705 62.1172932 4.31626253 14.39 <.0001 53.6575715 70.5770149 M200706 59.0197481 6.08883476 9.69 <.0001 47.0858476 70.9536486 M200707 87.0390086 7.52468696 11.57 <.0001 72.2908886 101.7871286 M200708 68.3076016 7.90233909 8.64 <.0001 52.8192968 83.7959064 M200709 72.2387667 5.08146719 14.22 <.0001 62.2792710 82.1982625 M200710 120.0423685 4.08551545 29.38 <.0001 112.0349029 128.0498341 M200711 127.3237273 6.62321303 19.22 <.0001 114.3424643 140.3049903 M200712 210.2224152 11.33994903 18.54 <.0001 187.9965166 232.4483137 M200801 200.3810629 11.66837922 17.17 <.0001 177.5114528 223.2506730 M200802 103.1046765 8.49205975 12.14 <.0001 86.4605401 119.7488129 M200803 105.3254416 6.24503215 16.87 <.0001 93.0853997 117.5654835 M200804 46.7696244 4.82526155 9.69 <.0001 37.3122826 56.2269662 M200805 85.1153385 2.19681950 38.74 <.0001 80.8096500 89.4210269 M200806 85.2156479 1.81986687 46.83 <.0001 81.6487733 88.7825225 M200807 129.4639689 2.22055816 58.30 <.0001 125.1117535 133.8161843 M200808 115.6960371 2.26469086 51.09 <.0001 111.2573232 120.1347510 M200809 54.8158997 1.80617691 30.35 <.0001 51.2758569 58.3559425 M200810 88.3327246 2.66362167 33.16 <.0001 83.1121205 93.5533288 M200811 107.6817283 3.92161926 27.46 <.0001 99.9954934 115.3679632 M200812 166.7081288 6.11245547 27.27 <.0001 154.7279325 178.6883251 M200901 142.7826723 5.92005460 24.12 <.0001 131.1795749 154.3857697 M200902 37.0323006 4.85325763 7.63 <.0001 27.5200875 46.5445137 M200903 80.5856757 4.26703892 18.89 <.0001 72.2224306 88.9489209 M200904 43.5072477 3.22276643 13.50 <.0001 37.1907396 49.8237558 M200905 88.7616644 2.31575009 38.33 <.0001 84.2228762 93.3004526 M200906 95.5851691 1.88866446 50.61 <.0001 91.8834536 99.2868846 M200907 153.7018568 2.05260698 74.88 <.0001 149.6788198 157.7248938 M200908 106.9263857 2.05839115 51.95 <.0001 102.8920120 110.9607595 M200909 42.2822707 1.83150494 23.09 <.0001 38.6925858 45.8719555 M200910 91.2765931 2.81255010 32.45 <.0001 85.7640945 96.7890917 M200911 82.3606189 4.63953676 17.75 <.0001 73.2672911 91.4539467 M200912 145.9492032 6.09371909 23.95 <.0001 134.0057295 157.8926768 M201001 122.8070578 5.71346517 21.49 <.0001 111.6088684 134.0052472 M201002 15.3625392 4.72378395 3.25 0.0011 6.1040900 24.6209885 M201003 63.0763603 4.50931048 13.99 <.0001 54.2382715 71.9144492 M201004 44.8874413 3.83446274 11.71 <.0001 37.3720301 52.4028525 M201005 58.4746351 2.96173110 19.74 <.0001 52.6697470 64.2795232 M201006 60.6085789 1.89845423 31.93 <.0001 56.8876758 64.3294820

84

Dependent Variable: kwh Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M201007 144.6660714 1.87486301 77.16 <.0001 140.9914063 148.3407365 M201008 99.1273952 1.83306118 54.08 <.0001 95.5346602 102.7201302 M201009 30.3250470 1.89488579 16.00 <.0001 26.6111380 34.0389561 M201010 58.3869497 2.55850359 22.82 <.0001 53.3723732 63.4015261 M201011 78.5495058 4.44547151 17.67 <.0001 69.8365390 87.2624725 M201012 140.6432891 5.72925510 24.55 <.0001 129.4141520 151.8724262 M201101 108.0310581 5.93910656 18.19 <.0001 96.3906196 119.6714967 M201102 7.5345836 5.12185866 1.47 0.1413 -2.5040780 17.5732452 M201103 65.7139511 4.75408910 13.82 <.0001 56.3961049 75.0317974 M201104 43.1942882 3.64021194 11.87 <.0001 36.0596017 50.3289747 M201105 66.3977587 3.18842158 20.82 <.0001 60.1485653 72.6469521 M201106 76.1913225 2.01746157 37.77 <.0001 72.2371692 80.1454757 M201107 121.7016408 1.89095735 64.36 <.0001 117.9954314 125.4078503 M201108 87.3278416 2.34376312 37.26 <.0001 82.7341489 91.9215343 cdd 1.5561178 0.02056372 75.67 <.0001 1.5158136 1.5964220 hdd 0.5595094 0.02049563 27.30 <.0001 0.5193387 0.5996801 cdd*post -0.2509664 0.02333723 -10.75 <.0001 -0.2967065 -0.2052262 hdd*post 0.0588381 0.02295552 2.56 0.0104 0.0138461 0.1038301 cdd*Treatment -0.0420991 0.00303768 -13.86 <.0001 -0.0480528 -0.0361453 hdd*Treatment -0.0089542 0.00210301 -4.26 <.0001 -0.0130760 -0.0048324 Other_dum -30.1329409 1.71003926 -17.62 <.0001 -33.4845573 -26.7813245 OtherAfter_dum -16.3135198 1.80921129 -9.02 <.0001 -19.8595098 -12.7675297 part -20.8944852 0.57874452 -36.10 <.0001 -22.0288040 -19.7601664

85

APPENDIX I: PERSISTENCE

Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 24718 279261511566 11297900.783 193.65 <.0001 Error 1.29E6 75551305180 58342.398075 Corrected Total 1.32E6 354812816747 R-Square Coeff Var Root MSE kwh Mean 0.787067 26.17322 241.5417 922.8583 Source DF Type I SS Mean Square F Value Pr > F cont_acc 24655 256514016557 10404137.763 178.33 <.0001 M200704 1 699011465.09 699011465.09 11981.2 <.0001 M200705 1 202555377.37 202555377.37 3471.84 <.0001 M200706 1 146607116.45 146607116.45 2512.87 <.0001 M200707 1 1329470171.3 1329470171.3 22787.4 <.0001 M200708 1 1541178680.3 1541178680.3 26416.1 <.0001 M200709 1 6699493.4609 6699493.4609 114.83 <.0001 M200710 1 474774130.6 474774130.6 8137.72 <.0001 M200711 1 106463980.5 106463980.5 1824.81 <.0001 M200712 1 582746876.16 582746876.16 9988.39 <.0001 M200801 1 595577573.39 595577573.39 10208.3 <.0001 M200802 1 26012179.169 26012179.169 445.85 <.0001 M200803 1 221066909.4 221066909.4 3789.13 <.0001 M200804 1 637757104.49 637757104.49 10931.3 <.0001 M200805 1 93321554.139 93321554.139 1599.55 <.0001 M200806 1 208658783.65 208658783.65 3576.45 <.0001 M200807 1 1647806472.2 1647806472.2 28243.7 <.0001 M200808 1 1676041782.9 1676041782.9 28727.7 <.0001 M200809 1 51467250.809 51467250.809 882.16 <.0001 M200810 1 317804351.03 317804351.03 5447.23 <.0001 M200811 1 135588112.87 135588112.87 2324.01 <.0001 M200812 1 581570613.39 581570613.39 9968.23 <.0001 M200901 1 362085439.52 362085439.52 6206.21 <.0001 M200902 1 110139124.08 110139124.08 1887.81 <.0001 M200903 1 135690786.66 135690786.66 2325.77 <.0001 M200904 1 555771463.45 555771463.45 9526.03 <.0001 M200905 1 116953100.07 116953100.07 2004.60 <.0001 M200906 1 9531585.8459 9531585.8459 163.37 <.0001 M200907 1 1567437166 1567437166 26866.2 <.0001 M200908 1 1145667141.6 1145667141.6 19637.0 <.0001 M200909 1 154025390.01 154025390.01 2640.03 <.0001 M200910 1 165774875.48 165774875.48 2841.41 <.0001

86

Dependent Variable: kwh Source DF Type I SS Mean Square F Value Pr > F M200911 1 16540945.708 16540945.708 283.52 <.0001 M200912 1 523470525.75 523470525.75 8972.39 <.0001 M201001 1 248599319.18 248599319.18 4261.04 <.0001 M201002 1 180595347 180595347 3095.44 <.0001 M201003 1 94203806.185 94203806.185 1614.67 <.0001 M201004 1 529937093.4 529937093.4 9083.22 <.0001 M201005 1 497306113.04 497306113.04 8523.92 <.0001 M201006 1 210585.45374 210585.45374 3.61 0.0575 M201007 1 802383394.18 802383394.18 13753.0 <.0001 M201008 1 201126179.29 201126179.29 3447.34 <.0001 M201009 1 13635566.164 13635566.164 233.72 <.0001 M201010 1 212904648.3 212904648.3 3649.23 <.0001 M201011 1 69918741.154 69918741.154 1198.42 <.0001 M201012 1 239261138.62 239261138.62 4100.98 <.0001 M201101 1 214666395.31 214666395.31 3679.42 <.0001 M201102 1 105209617.34 105209617.34 1803.31 <.0001 M201103 1 43932074.023 43932074.023 753.00 <.0001 M201104 1 782581733.97 782581733.97 13413.6 <.0001 M201105 1 1111845086.5 1111845086.5 19057.2 <.0001 M201106 1 186407690.44 186407690.44 3195.06 <.0001 M201107 1 110772896.43 110772896.43 1898.67 <.0001 M201108 1 317756797.04 317756797.04 5446.41 <.0001 cdd 1 390000836.57 390000836.57 6684.69 <.0001 hdd 1 131614709.52 131614709.52 2255.90 <.0001 cdd*post 1 74323.414215 74323.414215 1.27 0.2590 hdd*post 1 2430233.4516 2430233.4516 41.65 <.0001 cdd*Treatment 1 5866149.5622 5866149.5622 100.55 <.0001 hdd*Treatment 1 208402.7514 208402.7514 3.57 0.0588 Other_dum 1 83646971.236 83646971.236 1433.73 <.0001 OtherAfter_dum 1 1605677.1001 1605677.1001 27.52 <.0001 part_before 1 15154685.134 15154685.134 259.75 <.0001 part_after 1 8371274.3981 8371274.3981 143.49 <.0001 Source DF Type III SS Mean Square F Value Pr > F M200704 1 11518400.4 11518400.4 197.43 <.0001 M200705 1 13711517.2 13711517.2 235.02 <.0001 M200706 1 6385384.8 6385384.8 109.45 <.0001 M200707 1 6686316.2 6686316.2 114.60 <.0001 M200708 1 4670225.2 4670225.2 80.05 <.0001 M200709 1 11603887.2 11603887.2 198.89 <.0001 M200710 1 35875804.9 35875804.9 614.92 <.0001 M200711 1 13645555.3 13645555.3 233.89 <.0001 M200712 1 9988584.1 9988584.1 171.21 <.0001 M200801 1 8701377.3 8701377.3 149.14 <.0001 M200802 1 6198636.3 6198636.3 106.25 <.0001

87

Dependent Variable: kwh Source DF Type III SS Mean Square F Value Pr > F M200803 1 12093395.5 12093395.5 207.28 <.0001 M200804 1 8568206.1 8568206.1 146.86 <.0001 M200805 1 61754344.7 61754344.7 1058.48 <.0001 M200806 1 77526772.4 77526772.4 1328.82 <.0001 M200807 1 76288718.8 76288718.8 1307.60 <.0001 M200808 1 55925359.3 55925359.3 958.57 <.0001 M200809 1 38925967.4 38925967.4 667.20 <.0001 M200810 1 46953568.9 46953568.9 804.79 <.0001 M200811 1 18109277.4 18109277.4 310.40 <.0001 M200812 1 8152432.4 8152432.4 139.73 <.0001 M200901 1 6182452.5 6182452.5 105.97 <.0001 M200902 1 295707.2 295707.2 5.07 0.0244 M200903 1 7092166.3 7092166.3 121.56 <.0001 M200904 1 7785388.7 7785388.7 133.44 <.0001 M200905 1 62911769.7 62911769.7 1078.32 <.0001 M200906 1 117345179.9 117345179.9 2011.32 <.0001 M200907 1 160284792.4 160284792.4 2747.31 <.0001 M200908 1 73252938.6 73252938.6 1255.57 <.0001 M200909 1 20618589.0 20618589.0 353.41 <.0001 M200910 1 40045531.6 40045531.6 686.39 <.0001 M200911 1 4733747.7 4733747.7 81.14 <.0001 M200912 1 5775542.1 5775542.1 98.99 <.0001 M201001 1 4727431.6 4727431.6 81.03 <.0001 M201002 1 851.2 851.2 0.01 0.9039 M201003 1 2887571.9 2887571.9 49.49 <.0001 M201004 1 4127326.8 4127326.8 70.74 <.0001 M201005 1 16508338.7 16508338.7 282.96 <.0001 M201006 1 45903956.2 45903956.2 786.80 <.0001 M201007 1 212460993.2 212460993.2 3641.62 <.0001 M201008 1 119413992.6 119413992.6 2046.78 <.0001 M201009 1 14833555.6 14833555.6 254.25 <.0001 M201010 1 20638040.3 20638040.3 353.74 <.0001 M201011 1 4305318.7 4305318.7 73.79 <.0001 M201012 1 6004156.2 6004156.2 102.91 <.0001 M201101 1 2152606.2 2152606.2 36.90 <.0001 M201102 1 446660.9 446660.9 7.66 0.0057 M201103 1 1858401.4 1858401.4 31.85 <.0001 M201104 1 4556876.8 4556876.8 78.11 <.0001 M201105 1 16673500.4 16673500.4 285.79 <.0001 M201106 1 56069628.3 56069628.3 961.04 <.0001 M201107 1 144976127.1 144976127.1 2484.92 <.0001 M201108 1 54940933.6 54940933.6 941.70 <.0001 cdd 1 90045017.6 90045017.6 1543.39 <.0001 hdd 1 11017402.6 11017402.6 188.84 <.0001 cdd*post 1 32345.9 32345.9 0.55 0.4565 hdd*post 1 2525870.8 2525870.8 43.29 <.0001 cdd*Treatment 1 2180808.4 2180808.4 37.38 <.0001

88

Dependent Variable: kwh Source DF Type III SS Mean Square F Value Pr > F hdd*Treatment 1 69352.1 69352.1 1.19 0.2756 Other_dum 1 9383895.3 9383895.3 160.84 <.0001 OtherAfter_dum 1 1540318.9 1540318.9 26.40 <.0001 part_before 1 23519781.9 23519781.9 403.13 <.0001 persistence 1 8371274.4 8371274.4 143.49 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200704 108.8358779 7.74583313 14.05 <.0001 93.6543098 124.0174461 M200705 111.6041371 7.27997145 15.33 <.0001 97.3356419 125.8726323 M200706 112.9589909 10.79740537 10.46 <.0001 91.7964455 134.1215364 M200707 145.2642394 13.56929856 10.71 <.0001 118.6688781 171.8596007 M200708 127.8034970 14.28453024 8.95 <.0001 99.8063061 155.8006880 M200709 124.5665717 8.83266788 14.10 <.0001 107.2548446 141.8782988 M200710 168.7443438 6.80488101 24.80 <.0001 155.4070096 182.0816779 M200711 181.2608903 11.85224555 15.29 <.0001 158.0308941 204.4908864 M200712 272.6545556 20.83785113 13.08 <.0001 231.8130797 313.4960315 M200801 262.1140084 21.46289837 12.21 <.0001 220.0474612 304.1805555 M200802 159.2585523 15.45065244 10.31 <.0001 128.9758016 189.5413029 M200803 160.0296860 11.11523279 14.40 <.0001 138.2442097 181.8151623 M200804 100.8535231 8.32220212 12.12 <.0001 84.5422915 117.1647548 M200805 100.5471217 3.09049483 32.53 <.0001 94.4898574 106.6043859 M200806 83.6572544 2.29493238 36.45 <.0001 79.1592654 88.1552434 M200807 113.4227317 3.13662125 36.16 <.0001 107.2750613 119.5704021 M200808 99.6881733 3.21981845 30.96 <.0001 93.3774392 105.9989074 M200809 58.2580841 2.25542624 25.83 <.0001 53.8375258 62.6786425 M200810 113.0980076 3.98669295 28.37 <.0001 105.2842257 120.9117895 M200811 110.5557545 6.27513293 17.62 <.0001 98.2567085 122.8548005 M200812 119.0645161 10.07235182 11.82 <.0001 99.3230509 138.8059814 M200901 100.3199301 9.74538422 10.29 <.0001 81.2193101 119.4205500 M200902 17.7905584 7.90224988 2.25 0.0244 2.3024187 33.2786980 M200903 75.8516443 6.87967119 11.03 <.0001 62.3677239 89.3355646 M200904 57.9514628 5.01667991 11.55 <.0001 48.1189417 67.7839839 M200905 108.6345967 3.30821970 32.84 <.0001 102.1505991 115.1185942 M200906 108.4838185 2.41893653 44.85 <.0001 103.7427856 113.2248514 M200907 145.2236489 2.77066056 52.41 <.0001 139.7932489 150.6540489 M200908 98.4768054 2.77916009 35.43 <.0001 93.0297466 103.9238642 M200909 42.9377810 2.28403242 18.80 <.0001 38.4611555 47.4144064 M200910 111.5006559 4.25591091 26.20 <.0001 103.1592159 119.8420958 M200911 67.7736553 7.52402961 9.01 <.0001 53.0268145 82.5204962 M200912 99.8603229 10.03664879 9.95 <.0001 80.1888344 119.5318114 M201001 84.4554547 9.38225465 9.00 <.0001 66.0665563 102.8443531 M201002 -0.9262261 7.66829599 -0.12 0.9039 -15.9558241 14.1033719 M201003 51.3127248 7.29374903 7.04 <.0001 37.0172260 65.6082236 M201004 51.3550317 6.10577383 8.41 <.0001 39.3879237 63.3221397

89

Dependent Variable: kwh Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M201005 76.1048519 4.52431461 16.82 <.0001 67.2373499 84.9723539 M201006 67.6899556 2.41318885 28.05 <.0001 62.9601880 72.4197233 M201007 142.3906017 2.35957540 60.35 <.0001 137.7659146 147.0152889 M201008 100.6498487 2.22473176 45.24 <.0001 96.2894505 105.0102469 M201009 37.7430562 2.36704530 15.95 <.0001 33.1037283 42.3823841 M201010 70.2838085 3.73691481 18.81 <.0001 62.9595832 77.6080337 M201011 61.5582460 7.16598218 8.59 <.0001 47.5131659 75.6033262 M201012 95.2265338 9.38694179 10.14 <.0001 76.8284488 113.6246188 M201101 59.2515366 9.75459990 6.07 <.0001 40.1328543 78.3702190 M201102 -23.0897317 8.34491734 -2.77 0.0057 -39.4454845 -6.7339790 M201103 43.4788374 7.70371657 5.64 <.0001 28.3798163 58.5778586 M201104 50.7172203 5.73870551 8.84 <.0001 39.4695537 61.9648869 M201105 83.1779149 4.92024554 16.91 <.0001 73.5344019 92.8214280 M201106 81.7010780 2.63545792 31.00 <.0001 76.5356706 86.8664854 M201107 117.2236386 2.35157638 49.85 <.0001 112.6146293 121.8326480 M201108 78.2388965 2.54956939 30.69 <.0001 73.2418277 83.2359654 cdd 1.5018146 0.03822774 39.29 <.0001 1.4268896 1.5767397 hdd 0.5245347 0.03817040 13.74 <.0001 0.4497220 0.5993474 cdd*post -0.0316320 0.04248250 -0.74 0.4565 -0.1148963 0.0516322 hdd*post 0.2759150 0.04193357 6.58 <.0001 0.1937266 0.3581034 cdd*Treatment -0.0328086 0.00536626 -6.11 <.0001 -0.0433263 -0.0222910 hdd*Treatment -0.0040749 0.00373749 -1.09 0.2756 -0.0114002 0.0032504 Other_dum -37.5426327 2.96022885 -12.68 <.0001 -43.3445800 -31.7406853 OtherAfter_dum -15.9028703 3.09501202 -5.14 <.0001 -21.9689881 -9.8367526 part_before -21.7831673 1.08491701 -20.08 <.0001 -23.9095675 -19.6567670 part_after -14.9268633 1.24613446 -11.98 <.0001 -17.3692443 -12.4844824

90

APPENDIX J: PILOT MONTHLY REPORTS

Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 41742 368442971091 8826672.682 121.52 <.0001 Error 1.92E6 139676131298 72637.02443 Corrected Total 1.96E6 508119102388 R-Square Coeff Var Root MSE kwh Mean 0.725111 24.58442 269.5126 1096.274 Source DF Type I SS Mean Square F Value Pr > F cont_acc 41680 321169102589 7705592.6725 106.08 <.0001 M200704 1 1492870785.9 1492870785.9 20552.5 <.0001 M200705 1 372055965.16 372055965.16 5122.13 <.0001 M200706 1 417988345.12 417988345.12 5754.48 <.0001 M200707 1 3237679868.7 3237679868.7 44573.4 <.0001 M200708 1 3758164585.3 3758164585.3 51739.0 <.0001 M200709 1 33301161.18 33301161.18 458.46 <.0001 M200710 1 999704131.95 999704131.95 13763.0 <.0001 M200711 1 181615096.28 181615096.28 2500.31 <.0001 M200712 1 1749541002.6 1749541002.6 24086.1 <.0001 M200801 1 1869978842.5 1869978842.5 25744.2 <.0001 M200802 1 11612902.424 11612902.424 159.88 <.0001 M200803 1 404949730.32 404949730.32 5574.98 <.0001 M200804 1 1345807938.8 1345807938.8 18527.9 <.0001 M200805 1 194309569.36 194309569.36 2675.08 <.0001 M200806 1 462517081.42 462517081.42 6367.51 <.0001 M200807 1 3545896249.7 3545896249.7 48816.7 <.0001 M200808 1 3549421301.4 3549421301.4 48865.2 <.0001 M200809 1 120503827.4 120503827.4 1658.99 <.0001 M200810 1 658056875.31 658056875.31 9059.52 <.0001 M200811 1 272047045.24 272047045.24 3745.29 <.0001 M200812 1 1410663623.5 1410663623.5 19420.7 <.0001 M200901 1 875282571.02 875282571.02 12050.1 <.0001 M200902 1 204400571.9 204400571.9 2814.00 <.0001 M200903 1 264969667.53 264969667.53 3647.86 <.0001 M200904 1 1153765470.3 1153765470.3 15884.0 <.0001 M200905 1 237246503.64 237246503.64 3266.19 <.0001 M200906 1 15690551.078 15690551.078 216.01 <.0001 M200907 1 2925484594.5 2925484594.5 40275.4 <.0001 M200908 1 2143360384.3 2143360384.3 29507.8 <.0001 M200909 1 261119545.42 261119545.42 3594.85 <.0001 M200910 1 348997620.08 348997620.08 4804.68 <.0001

91

Dependent Variable: kwh Source DF Type I SS Mean Square F Value Pr > F M200911 1 30526473.049 30526473.049 420.26 <.0001 M200912 1 1061894154.1 1061894154.1 14619.2 <.0001 M201001 1 493924895.91 493924895.91 6799.91 <.0001 M201002 1 345648812.87 345648812.87 4758.58 <.0001 M201003 1 167721082.45 167721082.45 2309.03 <.0001 M201004 1 979862286.55 979862286.55 13489.8 <.0001 M201005 1 892534690.3 892534690.3 12287.6 <.0001 M201006 1 271269.1395 271269.1395 3.73 0.0533 M201007 1 1415066374.8 1415066374.8 19481.3 <.0001 M201008 1 376835854.79 376835854.79 5187.93 <.0001 M201009 1 21849053.395 21849053.395 300.80 <.0001 M201010 1 377594145.07 377594145.07 5198.37 <.0001 M201011 1 111110015.06 111110015.06 1529.66 <.0001 M201012 1 515165166.1 515165166.1 7092.32 <.0001 M201101 1 452958833.38 452958833.38 6235.92 <.0001 M201102 1 162028410.61 162028410.61 2230.66 <.0001 M201103 1 61227623.655 61227623.655 842.93 <.0001 M201104 1 1336379606.9 1336379606.9 18398.0 <.0001 M201105 1 1869372236.5 1869372236.5 25735.8 <.0001 M201106 1 328188443.92 328188443.92 4518.20 <.0001 M201107 1 188226242.23 188226242.23 2591.33 <.0001 M201108 1 574360974.77 574360974.77 7907.28 <.0001 cdd 1 628932101.93 628932101.93 8658.56 <.0001 hdd 1 164290694.94 164290694.94 2261.80 <.0001 cdd*post 1 9051727.778 9051727.778 124.62 <.0001 hdd*post 1 50484.766907 50484.766907 0.70 0.4045 cdd*Treatment 1 22001037.352 22001037.352 302.89 <.0001 hdd*Treatment 1 2889423.6536 2889423.6536 39.78 <.0001 Other_dum 1 99180741.34 99180741.34 1365.43 <.0001 OtherAfter_dum 1 3869768.8148 3869768.8148 53.28 <.0001 part 1 61852468.138 61852468.138 851.53 <.0001 Source DF Type III SS Mean Square F Value Pr > F M200704 1 5796262.3 5796262.3 79.80 <.0001 M200705 1 8676874.6 8676874.6 119.46 <.0001 M200706 1 3031786.6 3031786.6 41.74 <.0001 M200707 1 3875682.7 3875682.7 53.36 <.0001 M200708 1 1705681.1 1705681.1 23.48 <.0001 M200709 1 6163586.7 6163586.7 84.85 <.0001 M200710 1 33108892.4 33108892.4 455.81 <.0001 M200711 1 11876861.7 11876861.7 163.51 <.0001 M200712 1 11060886.9 11060886.9 152.28 <.0001 M200801 1 9257178.9 9257178.9 127.44 <.0001 M200802 1 3996478.0 3996478.0 55.02 <.0001 M200803 1 8590439.4 8590439.4 118.27 <.0001

92

Dependent Variable: kwh Source DF Type III SS Mean Square F Value Pr > F M200804 1 2291070.9 2291070.9 31.54 <.0001 M200805 1 103003136.8 103003136.8 1418.05 <.0001 M200806 1 210361439.8 210361439.8 2896.06 <.0001 M200807 1 227323170.2 227323170.2 3129.58 <.0001 M200808 1 169770379.9 169770379.9 2337.24 <.0001 M200809 1 94383053.5 94383053.5 1299.38 <.0001 M200810 1 55898457.0 55898457.0 769.56 <.0001 M200811 1 31614997.5 31614997.5 435.25 <.0001 M200812 1 32670710.6 32670710.6 449.78 <.0001 M200901 1 25135295.9 25135295.9 346.04 <.0001 M200902 1 2499240.4 2499240.4 34.41 <.0001 M200903 1 14335708.9 14335708.9 197.36 <.0001 M200904 1 7593450.4 7593450.4 104.54 <.0001 M200905 1 84828887.2 84828887.2 1167.85 <.0001 M200906 1 193026732.3 193026732.3 2657.42 <.0001 M200907 1 355061761.0 355061761.0 4888.17 <.0001 M200908 1 180544887.4 180544887.4 2485.58 <.0001 M200909 1 47220114.4 47220114.4 650.08 <.0001 M200910 1 45611431.0 45611431.0 627.94 <.0001 M200911 1 12292288.6 12292288.6 169.23 <.0001 M200912 1 23176952.5 23176952.5 319.08 <.0001 M201001 1 17995988.9 17995988.9 247.75 <.0001 M201002 1 247946.0 247946.0 3.41 0.0647 M201003 1 7204443.5 7204443.5 99.18 <.0001 M201004 1 4672963.5 4672963.5 64.33 <.0001 M201005 1 17119023.3 17119023.3 235.68 <.0001 M201006 1 76590596.9 76590596.9 1054.43 <.0001 M201007 1 426777936.6 426777936.6 5875.49 <.0001 M201008 1 230935417.8 230935417.8 3179.31 <.0001 M201009 1 22262696.0 22262696.0 306.49 <.0001 M201010 1 24486305.7 24486305.7 337.11 <.0001 M201011 1 11970325.0 11970325.0 164.80 <.0001 M201012 1 23460164.0 23460164.0 322.98 <.0001 M201101 1 13073388.7 13073388.7 179.98 <.0001 M201102 1 43848.9 43848.9 0.60 0.4372 M201103 1 6712808.6 6712808.6 92.42 <.0001 M201104 1 4601118.3 4601118.3 63.34 <.0001 M201105 1 16899481.4 16899481.4 232.66 <.0001 M201106 1 82299858.7 82299858.7 1133.03 <.0001 M201107 1 272337017.1 272337017.1 3749.29 <.0001 M201108 1 127457857.9 127457857.9 1754.72 <.0001 cdd 1 250931422.9 250931422.9 3454.59 <.0001 hdd 1 37818472.7 37818472.7 520.65 <.0001 cdd*post 1 9193036.4 9193036.4 126.56 <.0001 hdd*post 1 33571.8 33571.8 0.46 0.4966 cdd*Treatment 1 13043183.2 13043183.2 179.57 <.0001 hdd*Treatment 1 1972220.2 1972220.2 27.15 <.0001 Other_dum 1 8292685.8 8292685.8 114.17 <.0001 OtherAfter_dum 1 3933354.0 3933354.0 54.15 <.0001 part 1 61852468.1 61852468.1 851.53 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200704 62.7441163 7.02389504 8.93 <.0001 48.9775263 76.5107062 M200705 72.3121181 6.61619726 10.93 <.0001 59.3446016 85.2796347 M200706 60.9292012 9.43094453 6.46 <.0001 42.4448779 79.4135244 M200707 85.3477909 11.68415211 7.30 <.0001 62.4472592 108.2483226 M200708 59.5579986 12.29051841 4.85 <.0001 35.4690100 83.6469872 M200709 72.2918044 7.84786154 9.21 <.0001 56.9102687 87.6733401 M200710 133.7971950 6.26691356 21.35 <.0001 121.5142623 146.0801276 M200711 133.0911649 10.40824242 12.79 <.0001 112.6913718 153.4909581 M200712 221.8795715 17.98048349 12.34 <.0001 186.6384493 257.1206937

93

M200801 209.2024001 18.53132002 11.29 <.0001 172.8816574 245.5231427 M200802 99.6375758 13.43270459 7.42 <.0001 73.3099420 125.9652096 M200803 106.6021274 9.80250801 10.87 <.0001 87.3895526 125.8147021 M200804 42.0075130 7.47974392 5.62 <.0001 27.3474751 56.6675509 M200805 108.7780225 2.88865021 37.66 <.0001 103.1163686 114.4396765 M200806 113.2008140 2.10351448 53.82 <.0001 109.0779988 117.3236292 M200807 165.5632731 2.95951928 55.94 <.0001 159.7627183 171.3638280 M200808 147.4538300 3.05003006 48.35 <.0001 141.4758772 153.4317829 M200809 74.7511167 2.07371799 36.05 <.0001 70.6867016 78.8155319 M200810 105.0562118 3.78704834 27.74 <.0001 97.6337287 112.4786948 M200811 126.9648637 6.08577648 20.86 <.0001 115.0369535 138.8927739 M200812 209.7887663 9.89195111 21.21 <.0001 190.4008862 229.1766464 M200901 178.2198224 9.58060704 18.60 <.0001 159.4421658 196.9974790 M200902 45.3523902 7.73170335 5.87 <.0001 30.1985205 60.5062598 M200903 94.0504751 6.69468936 14.05 <.0001 80.9291168 107.1718334 M200904 49.3043419 4.82219322 10.22 <.0001 39.8530109 58.7556729 M200905 107.5991189 3.14858812 34.17 <.0001 101.4279957 113.7702421 M200906 116.7696591 2.26516741 51.55 <.0001 112.3300098 121.2093085 M200907 184.1000109 2.63318205 69.92 <.0001 178.9390657 189.2609562 M200908 131.7694897 2.64302511 49.86 <.0001 126.5892524 136.9497270 M200909 54.6513985 2.14346692 25.50 <.0001 50.4502779 58.8525191 M200910 101.9141215 4.06702216 25.06 <.0001 93.9428996 109.8853435 M200911 95.7670312 7.36170945 13.01 <.0001 81.3383367 110.1957256 M200912 176.3068836 9.87006759 17.86 <.0001 156.9618945 195.6518728 M201001 145.1263115 9.22013588 15.74 <.0001 127.0551659 163.1974572 M201002 13.8714688 7.50797614 1.85 0.0647 -0.8439033 28.5868409 M201003 70.9927246 7.12840959 9.96 <.0001 57.0212897 84.9641594 M201004 47.5683688 5.93063353 8.02 <.0001 35.9445333 59.1922042 M201005 66.7877523 4.35047181 15.35 <.0001 58.2609789 75.3145258 M201006 74.7491020 2.30195736 32.47 <.0001 70.2373457 79.2608584

94

Dependent Variable: kwh Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M201007 172.9842362 2.25675582 76.65 <.0001 168.5610733 177.4073991 M201008 121.7782356 2.15974973 56.39 <.0001 117.5452013 126.0112700 M201009 40.2076772 2.29667281 17.51 <.0001 35.7062784 44.7090760 M201010 66.2471156 3.60814816 18.36 <.0001 59.1752707 73.3189605 M201011 90.0216340 7.01250110 12.84 <.0001 76.2773757 103.7658922 M201012 166.6871565 9.27503673 17.97 <.0001 148.5084071 184.8659059 M201101 128.9103107 9.60887605 13.42 <.0001 110.0772478 147.7433735 M201102 6.3740742 8.20383129 0.78 0.4372 -9.7051498 22.4532981 M201103 72.8068675 7.57354543 9.61 <.0001 57.9629819 87.6507531 M201104 44.4229396 5.58154742 7.96 <.0001 33.4833008 55.3625784 M201105 72.5352540 4.75544836 15.25 <.0001 63.2147407 81.8557674 M201106 86.3364867 2.56492015 33.66 <.0001 81.3093324 91.3636410 M201107 140.7470985 2.29860917 61.23 <.0001 136.2419045 145.2522926 M201108 104.4972575 2.49459822 41.89 <.0001 99.6079318 109.3865833 cdd 1.8943330 0.03222982 58.78 <.0001 1.8311637 1.9575023 hdd 0.7460860 0.03269760 22.82 <.0001 0.6819999 0.8101722 cdd*post -0.4153082 0.03691646 -11.25 <.0001 -0.4876632 -0.3429532 hdd*post -0.0251381 0.03697629 -0.68 0.4966 -0.0976103 0.0473342 cdd*Treatment -0.0650964 0.00485785 -13.40 <.0001 -0.0746177 -0.0555752 hdd*Treatment -0.0176886 0.00339465 -5.21 <.0001 -0.0243420 -0.0110352 Other_dum -28.2565547 2.64454296 -10.68 <.0001 -33.4397669 -23.0733425 OtherAfter_dum -20.5141247 2.78772865 -7.36 <.0001 -25.9779759 -15.0502735 part -27.1425174 0.93014495 -29.18 <.0001 -28.9655692 -25.3194657

95

APPENDIX K: PILOT QUARTERLY REPORTS

Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 17237 20780433197 1205571 67.14 <.0001 Error 788357 14155273042 17955 Corrected Total 805594 34935706239 R-Square Coeff Var Root MSE kwh Mean 0.594819 26.86150 133.9978 498.8470 Source DF Type I SS Mean Square F Value Pr > F cont_acc 17175 15562076848 906089 50.46 <.0001 M200704 1 226793794 226793794 12630.9 <.0001 M200705 1 108188350 108188350 6025.39 <.0001 M200706 1 13421478 13421478 747.49 <.0001 M200707 1 287084277 287084277 15988.7 <.0001 M200708 1 362468264 362468264 20187.1 <.0001 M200709 1 142094 142094 7.91 0.0049 M200710 1 147096319 147096319 8192.31 <.0001 M200711 1 68103072 68103072 3792.90 <.0001 M200712 1 17560860 17560860 978.03 <.0001 M200801 1 17740547 17740547 988.03 <.0001 M200802 1 50344068 50344068 2803.84 <.0001 M200803 1 111897961 111897961 6231.99 <.0001 M200804 1 222651354 222651354 12400.2 <.0001 M200805 1 48190957 48190957 2683.92 <.0001 M200806 1 37771462 37771462 2103.63 <.0001 M200807 1 400939583 400939583 22329.7 <.0001 M200808 1 431013475 431013475 24004.7 <.0001 M200809 1 11289647 11289647 628.76 <.0001 M200810 1 91082770 91082770 5072.72 <.0001 M200811 1 55847105 55847105 3110.32 <.0001 M200812 1 30006909 30006909 1671.19 <.0001 M200901 1 11389007 11389007 634.29 <.0001 M200902 1 57511733 57511733 3203.03 <.0001 M200903 1 57160123 57160123 3183.45 <.0001 M200904 1 147093267 147093267 8192.14 <.0001 M200905 1 40093942 40093942 2232.97 <.0001 M200906 1 797175 797175 44.40 <.0001 M200907 1 379464422 379464422 21133.7 <.0001 M200908 1 268133213 268133213 14933.3 <.0001 M200909 1 40749138 40749138 2269.46 <.0001 M200910 1 38087604 38087604 2121.23 <.0001

96

Dependent Variable: kwh Source DF Type I SS Mean Square F Value Pr > F M200911 1 15998767 15998767 891.03 <.0001 M200912 1 39339890 39339890 2190.98 <.0001 M201001 1 17347247 17347247 966.13 <.0001 M201002 1 51902744 51902744 2890.65 <.0001 M201003 1 32768712 32768712 1825.00 <.0001 M201004 1 120147864 120147864 6691.46 <.0001 M201005 1 117019523 117019523 6517.23 <.0001 M201006 1 27110 27110 1.51 0.2192 M201007 1 180063368 180063368 10028.4 <.0001 M201008 1 38801123 38801123 2160.97 <.0001 M201009 1 2054229 2054229 114.41 <.0001 M201010 1 42663209 42663209 2376.06 <.0001 M201011 1 21478745 21478745 1196.23 <.0001 M201012 1 16813151 16813151 936.38 <.0001 M201101 1 11105612 11105612 618.51 <.0001 M201102 1 42416165 42416165 2362.31 <.0001 M201103 1 24698708 24698708 1375.56 <.0001 M201104 1 183553845 183553845 10222.8 <.0001 M201105 1 260846497 260846497 14527.5 <.0001 M201106 1 42991512 42991512 2394.35 <.0001 M201107 1 20247894 20247894 1127.68 <.0001 M201108 1 52144081 52144081 2904.09 <.0001 cdd 1 83724295 83724295 4662.90 <.0001 hdd 1 12288266 12288266 684.38 <.0001 cdd*post 1 1283719 1283719 71.49 <.0001 hdd*post 1 23797 23797 1.33 0.2496 cdd*Treatment 1 271653 271653 15.13 0.0001 hdd*Treatment 1 33819 33819 1.88 0.1699 Other_dum 1 4531076 4531076 252.35 <.0001 OtherAfter_dum 1 530 530 0.03 0.8636 part 1 1655222 1655222 92.19 <.0001 Source DF Type III SS Mean Square F Value Pr > F M200704 1 218.77 218.77 0.01 0.9121 M200705 1 500770.17 500770.17 27.89 <.0001 M200706 1 584401.49 584401.49 32.55 <.0001 M200707 1 193234.29 193234.29 10.76 0.0010 M200708 1 294740.11 294740.11 16.42 <.0001 M200709 1 62173.99 62173.99 3.46 0.0628 M200710 1 1275276.48 1275276.48 71.02 <.0001 M200711 1 577660.65 577660.65 32.17 <.0001 M200712 1 533580.78 533580.78 29.72 <.0001 M200801 1 443861.67 443861.67 24.72 <.0001 M200802 1 143420.68 143420.68 7.99 0.0047 M200803 1 398704.87 398704.87 22.21 <.0001

97

Dependent Variable: kwh Source DF Type III SS Mean Square F Value Pr > F M200804 1 14459.55 14459.55 0.81 0.3695 M200805 1 693886.59 693886.59 38.64 <.0001 M200806 1 1245305.24 1245305.24 69.36 <.0001 M200807 1 6853126.66 6853126.66 381.67 <.0001 M200808 1 5251176.33 5251176.33 292.46 <.0001 M200809 1 377.59 377.59 0.02 0.8847 M200810 1 1537738.37 1537738.37 85.64 <.0001 M200811 1 1876654.85 1876654.85 104.52 <.0001 M200812 1 1463543.99 1463543.99 81.51 <.0001 M200901 1 1148096.30 1148096.30 63.94 <.0001 M200902 1 62233.16 62233.16 3.47 0.0626 M200903 1 941933.14 941933.14 52.46 <.0001 M200904 1 201484.37 201484.37 11.22 0.0008 M200905 1 2093556.76 2093556.76 116.60 <.0001 M200906 1 5454534.43 5454534.43 303.78 <.0001 M200907 1 24863762.41 24863762.41 1384.75 <.0001 M200908 1 7613508.37 7613508.37 424.02 <.0001 M200909 1 97993.05 97993.05 5.46 0.0195 M200910 1 3365674.75 3365674.75 187.45 <.0001 M200911 1 959324.86 959324.86 53.43 <.0001 M200912 1 1778277.70 1778277.70 99.04 <.0001 M201001 1 1726664.50 1726664.50 96.16 <.0001 M201002 1 82112.07 82112.07 4.57 0.0325 M201003 1 831056.90 831056.90 46.28 <.0001 M201004 1 566193.91 566193.91 31.53 <.0001 M201005 1 803476.85 803476.85 44.75 <.0001 M201006 1 1825271.55 1825271.55 101.66 <.0001 M201007 1 29653521.51 29653521.51 1651.51 <.0001 M201008 1 8629484.57 8629484.57 480.61 <.0001 M201009 1 22761.00 22761.00 1.27 0.2602 M201010 1 1447092.60 1447092.60 80.59 <.0001 M201011 1 1445790.13 1445790.13 80.52 <.0001 M201012 1 2540488.09 2540488.09 141.49 <.0001 M201101 1 1383749.63 1383749.63 77.07 <.0001 M201102 1 51550.27 51550.27 2.87 0.0902 M201103 1 1099317.21 1099317.21 61.22 <.0001 M201104 1 772982.33 772982.33 43.05 <.0001 M201105 1 1787316.79 1787316.79 99.54 <.0001 M201106 1 7776876.99 7776876.99 433.12 <.0001 M201107 1 26405902.39 26405902.39 1470.64 <.0001 M201108 1 8992461.00 8992461.00 500.82 <.0001 cdd 1 30197889.46 30197889.46 1681.83 <.0001 hdd 1 2190826.96 2190826.96 122.01 <.0001 cdd*post 1 1266129.11 1266129.11 70.52 <.0001 hdd*post 1 24707.27 24707.27 1.38 0.2408 cdd*Treatment 1 135699.25 135699.25 7.56 0.0060 hdd*Treatment 1 18795.76 18795.76 1.05 0.3062 Other_dum 1 915569.53 915569.53 50.99 <.0001 OtherAfter_dum 1 301.18 301.18 0.02 0.8970 part 1 1655222.17 1655222.17 92.19 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200704 -0.59304228 5.37266808 -0.11 0.9121 -11.12329438 9.93720983 M200705 -27.23730755 5.15754006 -5.28 <.0001 -37.34591584 -17.12869925 M200706 -42.80169812 7.50244819 -5.71 <.0001 -57.50624894 -28.09714730 M200707 -30.79575749 9.38742441 -3.28 0.0010 -49.19479950 -12.39671548 M200708 -39.94187913 9.85840223 -4.05 <.0001 -59.26402211 -20.61973615 M200709 -11.42865174 6.14169550 -1.86 0.0628 -23.46617220 0.60886872 M200710 40.48934472 4.80436765 8.43 <.0001 31.07294270 49.90574674 M200711 44.90562716 7.91702533 5.67 <.0001 29.38851882 60.42273550 M200712 74.46236135 13.65948660 5.45 <.0001 47.69021847 101.23450423

98

M200801 69.64010036 14.00661613 4.97 <.0001 42.18759505 97.09260568 M200802 28.70964568 10.15826660 2.83 0.0047 8.79977844 48.61951293 M200803 35.09021488 7.44659817 4.71 <.0001 20.49512826 49.68530151 M200804 -5.13870396 5.72629656 -0.90 0.3695 -16.36205622 6.08464829 M200805 14.08583298 2.26587501 6.22 <.0001 9.64479275 18.52687321 M200806 13.57132104 1.62960323 8.33 <.0001 10.37735249 16.76528958 M200807 44.24262280 2.26461379 19.54 <.0001 39.80405452 48.68119108 M200808 39.86925840 2.33135021 17.10 <.0001 35.29988893 44.43862787 M200809 -0.23373137 1.61176701 -0.15 0.8847 -3.39274151 2.92527877 M200810 27.36153414 2.95662932 9.25 <.0001 21.56663825 33.15643002 M200811 47.28711547 4.62538919 10.22 <.0001 38.22150532 56.35272562 M200812 68.04642726 7.53702748 9.03 <.0001 53.27410218 82.81875235 M200901 57.95444608 7.24761746 8.00 <.0001 43.74935508 72.15953709 M200902 10.90251938 5.85616880 1.86 0.0626 -0.57537818 22.38041693 M200903 36.89285359 5.09365906 7.24 <.0001 26.90944996 46.87625722 M200904 12.47304274 3.72348315 3.35 0.0008 5.17513866 19.77094682 M200905 26.13440806 2.42029308 10.80 <.0001 21.39071350 30.87810261 M200906 30.82319209 1.76846484 17.43 <.0001 27.35705937 34.28932482 M200907 74.52007814 2.00256923 37.21 <.0001 70.59510855 78.44504774 M200908 41.59662151 2.02005552 20.59 <.0001 37.63737937 45.55586365 M200909 3.89868403 1.66885295 2.34 0.0195 0.62778733 7.16958072 M200910 43.50172266 3.17737186 13.69 <.0001 37.27417870 49.72926663 M200911 40.78321080 5.57951213 7.31 <.0001 29.84755119 51.71887041 M200912 74.61464097 7.49759407 9.95 <.0001 59.91960406 89.30967788 M201001 68.61001386 6.99650565 9.81 <.0001 54.89709372 82.32293400 M201002 12.16974942 5.69083234 2.14 0.0325 1.01590586 23.32359298 M201003 36.82989643 5.41355783 6.80 <.0001 26.21950177 47.44029109 M201004 25.36759158 4.51745987 5.62 <.0001 16.51351935 34.22166381 M201005 22.57574759 3.37483946 6.69 <.0001 15.96117363 29.19032155 M201006 18.05766847 1.79100004 10.08 <.0001 14.54736751 21.56796942

99

Dependent Variable: kwh Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M201007 70.87079948 1.74392119 40.64 <.0001 67.45277150 74.28882746 M201008 36.88526264 1.68251178 21.92 <.0001 33.58759509 40.18293019 M201009 -2.01119725 1.78630944 -1.13 0.2602 -5.51230479 1.48991029 M201010 25.29596348 2.81773875 8.98 <.0001 19.77328853 30.81863843 M201011 48.01900590 5.35128644 8.97 <.0001 37.53066110 58.50735070 M201012 83.07799947 6.98433577 11.89 <.0001 69.38893189 96.76706705 M201101 64.07358812 7.29874091 8.78 <.0001 49.76829684 78.37887941 M201102 10.53469833 6.21733537 1.69 0.0902 -1.65107378 22.72047044 M201103 44.74801647 5.71886622 7.82 <.0001 33.53922744 55.95680551 M201104 28.02672098 4.27154678 6.56 <.0001 19.65463028 36.39881168 M201105 36.81806512 3.69026756 9.98 <.0001 29.58526251 44.05086773 M201106 41.16041496 1.97776547 20.81 <.0001 37.28405991 45.03677000 M201107 67.93028809 1.77137541 38.35 <.0001 64.45845076 71.40212542 M201108 42.82777401 1.91374418 22.38 <.0001 39.07689859 46.57864943 cdd 1.06392641 0.02594305 41.01 <.0001 1.01307890 1.11477392 hdd 0.27270888 0.02468840 11.05 <.0001 0.22432043 0.32109732 cdd*post -0.24660279 0.02936680 -8.40 <.0001 -0.30416076 -0.18904483 hdd*post 0.03277928 0.02794375 1.17 0.2408 -0.02198955 0.08754812 cdd*Treatment -0.01028141 0.00373991 -2.75 0.0060 -0.01761151 -0.00295130 hdd*Treatment -0.00267976 0.00261917 -1.02 0.3062 -0.00781325 0.00245373 Other_dum -17.66534230 2.47385393 -7.14 <.0001 -22.51401435 -12.81667025 OtherAfter_dum -0.34509292 2.66453338 -0.13 0.8970 -5.56749040 4.87730457 part -6.93702542 0.72250823 -9.60 <.0001 -8.35311771 -5.52093313

100

APPENDIX K-1: PERSISTENCE BY MONTH

MonthID Estimate Standard

Error t Value Pr > |t| 95% Confidence Limits

200804 -2.56042 3.37253 -0.76 0.4477 -9.17046 4.049622 200805 -11.0401 3.357688 -3.29 0.001 -17.621 -4.45913 200806 -21.624 3.481608 -6.21 <.0001 -28.4479 -14.8002 200807 -31.0054 3.814712 -8.13 <.0001 -38.4821 -23.5286 200808 -27.4054 3.847761 -7.12 <.0001 -34.9469 -19.8639 200809 -22.9903 3.427343 -6.71 <.0001 -29.7077 -16.2728 200810 -14.4567 3.446244 -4.19 <.0001 -21.2112 -7.70221 200811 -17.728 3.349711 -5.29 <.0001 -24.2933 -11.1627 200812 -25.1652 3.691986 -6.82 <.0001 -32.4014 -17.9291 200901 -22.9291 3.626267 -6.32 <.0001 -30.0365 -15.8218 200902 -19.5979 3.381828 -5.8 <.0001 -26.2261 -12.9696 200903 -19.1521 3.341095 -5.73 <.0001 -25.7005 -12.6037 200904 -19.0588 3.367331 -5.66 <.0001 -25.6586 -12.4589 200905 -21.153 3.373886 -6.27 <.0001 -27.7657 -14.5403 200906 -24.5979 3.382502 -7.27 <.0001 -31.2274 -17.9683 200907 -34.9594 3.681973 -9.49 <.0001 -42.1759 -27.7428 200908 -31.4964 3.688775 -8.54 <.0001 -38.7263 -24.2665 200909 -29.3036 3.47225 -8.44 <.0001 -36.1091 -22.4981 200910 -19.8679 3.385103 -5.87 <.0001 -26.5026 -13.2332 200911 -27.2627 3.357631 -8.12 <.0001 -33.8436 -20.6819 200912 -27.8593 3.683478 -7.56 <.0001 -35.0788 -20.6398 201001 -26.746 3.565954 -7.5 <.0001 -33.7351 -19.7568 201002 -19.1986 3.366944 -5.7 <.0001 -25.7977 -12.5995 201003 -21.4866 3.349718 -6.41 <.0001 -28.052 -14.9213 201004 -16.5221 3.380358 -4.89 <.0001 -23.1474 -9.89667 201005 -19.541 3.399455 -5.75 <.0001 -26.2039 -12.8782 201006 -22.0209 3.365429 -6.54 <.0001 -28.617 -15.4247 201007 -28.1531 3.520667 -8 <.0001 -35.0535 -21.2527 201008 -22.3758 3.43685 -6.51 <.0001 -29.1119 -15.6397 201009 -17.6135 3.400585 -5.18 <.0001 -24.2785 -10.9484 201010 -15.5193 3.372691 -4.6 <.0001 -22.1296 -8.9089 201011 -17.7206 3.369074 -5.26 <.0001 -24.3238 -11.1173 201012 -19.8862 3.596843 -5.53 <.0001 -26.9359 -12.8365 201101 -12.5272 3.66508 -3.42 0.0006 -19.7107 -5.3438 201102 -10.2223 3.467588 -2.95 0.0032 -17.0187 -3.42594 201103 -15.402 3.417295 -4.51 <.0001 -22.0998 -8.70422 201104 -13.8252 3.45332 -4 <.0001 -20.5936 -7.05678 201105 -15.2525 3.520533 -4.33 <.0001 -22.1526 -8.35236 201106 -12.1282 3.403844 -3.56 0.0004 -18.7996 -5.45679 201107 -18.1355 3.592495 -5.05 <.0001 -25.1766 -11.0943 201108 -18.9297 3.68172 -5.14 <.0001 -26.1457 -11.7136 201109 -4.25117 3.519981 -1.21 0.2272 -11.1502 2.647868

101

APPENDIX K-2: PILOT MONTHLY REPORT SAVING BY MONTH



200804 -2.59954 2.914822 -0.89 0.3725 -8.31249 3.113407 200805 -14.9656 2.914333 -5.14 <.0001 -20.6775 -9.25356 200806 -25.6153 3.034874 -8.44 <.0001 -31.5636 -19.6671 200807 -33.1212 3.332642 -9.94 <.0001 -39.653 -26.5893 200808 -30.8517 3.374252 -9.14 <.0001 -37.4651 -24.2382 200809 -23.6327 3.028296 -7.8 <.0001 -29.568 -17.6973 200810 -16.9747 3.057302 -5.55 <.0001 -22.9669 -10.9824 200811 -23.8746 2.986512 -7.99 <.0001 -29.7281 -18.0211 200812 -31.3931 3.281422 -9.57 <.0001 -37.8246 -24.9616 200901 -29.1572 3.23806 -9 <.0001 -35.5037 -22.8107 200902 -24.1163 3.044807 -7.92 <.0001 -30.084 -18.1486 200903 -26.3186 3.021511 -8.71 <.0001 -32.2407 -20.3965 200904 -25.1259 3.054717 -8.23 <.0001 -31.113 -19.1388 200905 -25.6602 3.070572 -8.36 <.0001 -31.6784 -19.642 200906 -28.8764 3.090542 -9.34 <.0001 -34.9338 -22.8191 200907 -37.6293 3.34816 -11.24 <.0001 -44.1916 -31.067 200908 -33.497 3.364963 -9.95 <.0001 -40.0922 -26.9018 200909 -31.595 3.195934 -9.89 <.0001 -37.8589 -25.3311 200910 -22.1255 3.138598 -7.05 <.0001 -28.277 -15.974 200911 -28.6667 3.128058 -9.16 <.0001 -34.7976 -22.5359 200912 -33.7281 3.396351 -9.93 <.0001 -40.3848 -27.0714 201001 -36.0491 3.31136 -10.89 <.0001 -42.5393 -29.559 201002 -26.1323 3.163126 -8.26 <.0001 -32.3319 -19.9327 201003 -30.652 3.161303 -9.7 <.0001 -36.848 -24.4559 201004 -23.0317 3.198 -7.2 <.0001 -29.2997 -16.7637 201005 -24.5352 3.224076 -7.61 <.0001 -30.8543 -18.2161 201006 -25.3486 3.208008 -7.9 <.0001 -31.6362 -19.0611 201007 -32.9753 3.336432 -9.88 <.0001 -39.5146 -26.436 201008 -27.0438 3.276748 -8.25 <.0001 -33.4661 -20.6215 201009 -26.0214 3.252183 -8 <.0001 -32.3955 -19.6472 201010 -26.419 3.234013 -8.17 <.0001 -32.7575 -20.0804 201011 -34.4198 3.236742 -10.63 <.0001 -40.7637 -28.0759 201012 -43.9259 3.42217 -12.84 <.0001 -50.6332 -37.2185 201101 -35.972 3.473648 -10.36 <.0001 -42.7802 -29.1638 201102 -28.6339 3.322726 -8.62 <.0001 -35.1464 -22.1215 201103 -34.955 3.288527 -10.63 <.0001 -41.4004 -28.5096 201104 -27.7705 3.319837 -8.37 <.0001 -34.2773 -21.2637 201105 -30.7977 3.375258 -9.12 <.0001 -37.4131 -24.1823 201106 -29.8719 3.287211 -9.09 <.0001 -36.3147 -23.4291 201107 -35.4358 3.43776 -10.31 <.0001 -42.1737 -28.6979 201108 -34.7696 3.512034 -9.9 <.0001 -41.653 -27.8861 201109 -16.3565 3.386699 -4.83 <.0001 -22.9943 -9.71867

102

APPENDIX K-3: PILOT QUARTERLY REPORT SAVING BY MONTH



200804 -1.35631 2.260743 -0.6 0.5485 -5.78729 3.074676 200805 -3.95038 2.264511 -1.74 0.0811 -8.38874 0.487991 200806 -6.09273 2.355996 -2.59 0.0097 -10.7104 -1.47506 200807 -5.22187 2.58664 -2.02 0.0435 -10.2916 -0.15214 200808 -7.64777 2.617295 -2.92 0.0035 -12.7776 -2.51795 200809 -7.28662 2.354311 -3.1 0.002 -11.901 -2.67225 200810 -3.7265 2.380418 -1.57 0.1175 -8.39204 0.939044 200811 -4.71125 2.322587 -2.03 0.0425 -9.26344 -0.15905 200812 -6.59479 2.55471 -2.58 0.0098 -11.6019 -1.58764 200901 -7.393 2.512643 -2.94 0.0033 -12.3177 -2.4683 200902 -5.92535 2.364609 -2.51 0.0122 -10.5599 -1.2908 200903 -6.37578 2.347543 -2.72 0.0066 -10.9769 -1.77468 200904 -5.96499 2.370576 -2.52 0.0119 -10.6112 -1.31874 200905 -7.39045 2.384038 -3.1 0.0019 -12.0631 -2.71782 200906 -9.4077 2.400045 -3.92 <.0001 -14.1117 -4.70369 200907 -12.1034 2.595234 -4.66 <.0001 -17.1899 -7.0168 200908 -11.9266 2.610943 -4.57 <.0001 -17.044 -6.80927 200909 -11.18 2.490594 -4.49 <.0001 -16.0615 -6.2985 200910 -5.65718 2.444853 -2.31 0.0207 -10.449 -0.86535 200911 -9.17382 2.432316 -3.77 0.0002 -13.9411 -4.40656 200912 -9.63302 2.64367 -3.64 0.0003 -14.8145 -4.45151 201001 -8.20133 2.576238 -3.18 0.0015 -13.2507 -3.15199 201002 -5.31968 2.459501 -2.16 0.0305 -10.1402 -0.49914 201003 -6.69734 2.457517 -2.73 0.0064 -11.514 -1.88069 201004 -6.20246 2.485276 -2.5 0.0126 -11.0735 -1.3314 201005 -5.89326 2.507505 -2.35 0.0188 -10.8079 -0.97864 201006 -8.92603 2.497386 -3.57 0.0004 -13.8208 -4.03123 201007 -12.4227 2.594498 -4.79 <.0001 -17.5078 -7.33752 201008 -11.333 2.54666 -4.45 <.0001 -16.3244 -6.34166 201009 -7.85042 2.5293 -3.1 0.0019 -12.8078 -2.89308 201010 -6.57596 2.515825 -2.61 0.009 -11.5069 -1.64503 201011 -9.25854 2.517608 -3.68 0.0002 -14.193 -4.32411 201012 -9.26541 2.655019 -3.49 0.0005 -14.4692 -4.06166 201101 -8.97139 2.702695 -3.32 0.0009 -14.2686 -3.6742 201102 -7.89969 2.583526 -3.06 0.0022 -12.9633 -2.83607 201103 -8.8372 2.55504 -3.46 0.0005 -13.845 -3.8294 201104 -5.81176 2.579995 -2.25 0.0243 -10.8685 -0.75506 201105 -4.17114 2.623834 -1.59 0.1119 -9.31377 0.971487 201106 -4.45817 2.558144 -1.74 0.0814 -9.47205 0.555706 201107 -6.8993 2.671703 -2.58 0.0098 -12.1358 -1.66285 201108 -8.83062 2.729787 -3.23 0.0012 -14.1809 -3.48033 201109 -1.74767 2.637858 -0.66 0.5076 -6.91779 3.422442

103

APPENDIX K-4: UCLA SELECTION SAVING BY MONTH



201010 -8.57228 6.124721 -1.4 0.1616 -20.5766 3.43203 201011 -17.8662 6.043635 -2.96 0.0031 -29.7115 -6.02078 201012 -23.2021 6.487012 -3.58 0.0003 -35.9165 -10.4877 201101 -24.3131 6.700499 -3.63 0.0003 -37.4459 -11.1803 201102 -18.2243 6.222235 -2.93 0.0034 -30.4197 -6.02888 201103 -19.9688 6.140745 -3.25 0.0011 -32.0045 -7.93306 201104 -24.1562 6.474384 -3.73 0.0002 -36.8459 -11.4666 201105 -23.7368 6.654881 -3.57 0.0004 -36.7802 -10.6934 201106 -25.1177 6.20469 -4.05 <.0001 -37.2787 -12.9566 201107 -30.7324 6.859697 -4.48 <.0001 -44.1773 -17.2876 201108 -28.0004 7.152001 -3.92 <.0001 -42.0182 -13.9827 201109 -24.7745 6.592342 -3.76 0.0002 -37.6954 -11.8537

104

APPENDIX K-5: SMUD SEGMENTATION SAVING BY MONTH



201010 -5.69068 7.100696 -0.8 0.4229 -19.6079 8.22652 201011 -22.6443 6.985083 -3.24 0.0012 -36.3349 -8.95368 201012 -29.0831 7.503739 -3.88 0.0001 -43.7903 -14.376 201101 -31.9295 7.708584 -4.14 <.0001 -47.0382 -16.8209 201102 -26.9691 7.211699 -3.74 0.0002 -41.1038 -12.8343 201103 -26.1106 7.114965 -3.67 0.0002 -40.0558 -12.1654 201104 -16.4712 7.491859 -2.2 0.0279 -31.1551 -1.78737 201105 -18.4277 7.735524 -2.38 0.0172 -33.5892 -3.26626 201106 -18.2423 7.20801 -2.53 0.0114 -32.3699 -4.11481 201107 -20.4894 7.924004 -2.59 0.0097 -36.0203 -4.95853 201108 -24.6576 8.303777 -2.97 0.003 -40.9329 -8.38243 201109 -20.1751 7.648756 -2.64 0.0083 -35.1664 -5.18367

105

APPENDIX K-6: HIGH USE SAVING BY MONTH



201010 -16.7154 7.347733 -2.27 0.0229 -31.1167 -2.31397 201011 -26.5882 7.244084 -3.67 0.0002 -40.7864 -12.3899 201012 -34.7052 7.771316 -4.47 <.0001 -49.9368 -19.4736 201101 -40.6334 7.994445 -5.08 <.0001 -56.3024 -24.9645 201102 -39.1932 7.488569 -5.23 <.0001 -53.8706 -24.5158 201103 -37.229 7.387894 -5.04 <.0001 -51.7091 -22.7489 201104 -39.7277 7.785532 -5.1 <.0001 -54.9871 -24.4682 201105 -42.9756 8.029938 -5.35 <.0001 -58.7141 -27.2371 201106 -38.4145 7.503913 -5.12 <.0001 -53.1219 -23.707 201107 -33.5204 8.257003 -4.06 <.0001 -49.7039 -17.3369 201108 -44.2956 8.635833 -5.13 <.0001 -61.2216 -27.3696 201109 -45.6131 7.986644 -5.71 <.0001 -61.2667 -29.9594

106

APPENDIX K-7: E-REPORTS SAVING BY MONTH



201010 -6.74993 4.15104536 -1.63 0.1039 -14.8859 1.386013 201011 -15.3473 4.08996649 -3.75 0.0002 -23.3635 -7.33109 201012 -13.5093 4.37842902 -3.09 0.002 -22.0909 -4.92771 201101 -15.8781 4.52347977 -3.51 0.0004 -24.744 -7.0122 201102 -18.156 4.23750813 -4.28 <.0001 -26.4614 -9.85058 201103 -13.5246 4.17396842 -3.24 0.0012 -21.7054 -5.34369 201104 -12.6248 4.39618842 -2.87 0.0041 -21.2412 -4.00834 201105 -9.07715 4.53161851 -2 0.0452 -17.959 -0.1953 201106 -17.4858 4.24916388 -4.12 <.0001 -25.814 -9.15752 201107 -15.5102 4.66083983 -3.33 0.0009 -24.6453 -6.37509 201108 -15.5371 4.86902729 -3.19 0.0014 -25.0803 -5.99397 201109 -17.4317 4.52276531 -3.85 0.0001 -26.2962 -8.56716

107

APPENDIX K-8: SEASONAL BURST SAVING BY MONTH


Error t Value Pr > |t| 95% Confiden ce Limits

201105 -6.06238 5.284226 -1.15 0.2513 -16.4193 4.2945409 201106 -12.5682 5.120763 -2.45 0.0141 -22.60476 -2.5316843 201107 -18.6472 5.644213 -3.3 0.001 -29.709705 -7.5847374 201108 -24.6482 5.877526 -4.19 <.0001 -36.167941 -13.1283989 201109 -21.3041 5.460017 -3.9 <.0001 -32.005558 -10.6026251

108

APPENDIX K-9: WAVE 1 SAVING BY MONTH

Dependent Variable: kwh


Error t Value Pr > |t| 95% Confidence

200804 -2.024957 1.78392769 -1.14 0.2563 -5.52139 1.471478 200805 -14.00575 1.79417732 -7.81 <.0001 -17.5223 -10.4892 200806 -22.55028 1.79962794 -12.53 <.0001 -26.0775 -19.0231 200807 -32.41978 1.80082091 -18 <.0001 -35.9493 -28.8902 200808 -30.44148 1.81217719 -16.8 <.0001 -33.9933 -26.8897 200809 -23.68814 1.81637547 -13.04 <.0001 -27.2482 -20.1281 200810 -16.4302 1.82261058 -9.01 <.0001 -20.0025 -12.8579 200811 -17.49649 1.8270394 -9.58 <.0001 -21.0774 -13.9156 200812 -19.43768 1.83386382 -10.6 <.0001 -23.032 -15.8434 200901 -17.64918 1.83579291 -9.61 <.0001 -21.2473 -14.0511 200902 -15.38102 1.83807271 -8.37 <.0001 -18.9836 -11.7785 200903 -18.999 1.8459301 -10.29 <.0001 -22.617 -15.381 200904 -18.53856 1.85142782 -10.01 <.0001 -22.1673 -14.9098 200905 -22.71851 1.85508445 -12.25 <.0001 -26.3544 -19.0826 200906 -25.92992 1.86103428 -13.93 <.0001 -29.5775 -22.2824 200907 -35.42592 1.86510919 -18.99 <.0001 -39.0815 -31.7704 200908 -34.59813 1.87086387 -18.49 <.0001 -38.265 -30.9313 200909 -29.83606 1.8735772 -15.92 <.0001 -33.5082 -26.1639 200910 -18.11719 1.88499195 -9.61 <.0001 -21.8117 -14.4227 200911 -21.4856 1.88322267 -11.41 <.0001 -25.1767 -17.7946 200912 -23.45941 1.88824489 -12.42 <.0001 -27.1603 -19.7585 201001 -23.49203 1.8894893 -12.43 <.0001 -27.1954 -19.7887 201002 -17.32025 1.89367062 -9.15 <.0001 -21.0318 -13.6087 201003 -20.61004 1.89812847 -10.86 <.0001 -24.3303 -16.8898 201004 -16.98288 1.90607745 -8.91 <.0001 -20.7187 -13.247 201005 -18.55161 1.91440063 -9.69 <.0001 -22.3038 -14.7995 201006 -24.26543 1.91353758 -12.68 <.0001 -28.0159 -20.515 201007 -31.39947 1.91801678 -16.37 <.0001 -35.1587 -27.6402 201008 -27.49693 1.92707721 -14.27 <.0001 -31.2739 -23.7199 201009 -22.31961 1.93053838 -11.56 <.0001 -26.1034 -18.5358 201010 -20.57634 1.93911402 -10.61 <.0001 -24.3769 -16.7757 201011 -20.60317 1.94276155 -10.61 <.0001 -24.4109 -16.7954 201012 -24.65674 1.94315647 -12.69 <.0001 -28.4653 -20.8482 201101 -19.22048 1.94684898 -9.87 <.0001 -23.0362 -15.4047 201102 -14.65022 1.94997113 -7.51 <.0001 -18.4721 -10.8283 201103 -21.66219 1.96064128 -11.05 <.0001 -25.505 -17.8194 201104 -17.50822 1.9601453 -8.93 <.0001 -21.35 -13.6664 201105 -19.83692 1.9704405 -10.07 <.0001 -23.6989 -15.9749 201106 -22.14334 1.97458785 -11.21 <.0001 -26.0135 -18.2732 201107 -27.91041 1.97785607 -14.11 <.0001 -31.7869 -24.0339 201108 -29.18001 1.97894412 -14.75 <.0001 -33.0587 -25.3014 201109 -10.73558 1.98468358 -5.41 <.0001 -14.6255 -6.84567

109

APPENDIX K-10: WAVE 1 SAVING BY YEAR

Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 83577 850415255472 10175230.691 176.05 <.0001 Error 3.92E6 226596767053 57796.937803 Corrected Total 4E6 1.077012E12 R-Square Coeff Var Root MSE kwh Mean 0.789606 26.09477 240.4099 921.2955 Source DF Type I SS Mean Square F Value Pr > F cont_acc 83512 780111781884 9341313.6062 161.62 <.0001 M200704 1 2319721482 2319721482 40135.7 <.0001 M200705 1 663360401.33 663360401.33 11477.4 <.0001 M200706 1 529592063.97 529592063.97 9162.98 <.0001 M200707 1 4630798576.8 4630798576.8 80121.9 <.0001 M200708 1 5427031663.5 5427031663.5 93898.3 <.0001 M200709 1 36892069.53 36892069.53 638.30 <.0001 M200710 1 1540925594 1540925594 26661.0 <.0001 M200711 1 340376609.68 340376609.68 5889.18 <.0001 M200712 1 2025616037 2025616037 35047.1 <.0001 M200801 1 2134057597.3 2134057597.3 36923.4 <.0001 M200802 1 62429615.113 62429615.113 1080.15 <.0001 M200803 1 707206319.63 707206319.63 12236.1 <.0001 M200804 1 2108239752.8 2108239752.8 36476.7 <.0001 M200805 1 315165995.1 315165995.1 5452.99 <.0001 M200806 1 694212589.32 694212589.32 12011.2 <.0001 M200807 1 5444117061.5 5444117061.5 94193.9 <.0001 M200808 1 5535838781.8 5535838781.8 95780.8 <.0001 M200809 1 192457078.19 192457078.19 3329.88 <.0001 M200810 1 988225575.71 988225575.71 17098.2 <.0001 M200811 1 426783228.25 426783228.25 7384.18 <.0001 M200812 1 1840750633.2 1840750633.2 31848.6 <.0001 M200901 1 1130758412.7 1130758412.7 19564.3 <.0001 M200902 1 337735434.39 337735434.39 5843.48 <.0001 M200903 1 415679969.5 415679969.5 7192.08 <.0001 M200904 1 1718765931 1718765931 29738.0 <.0001 M200905 1 352803332.42 352803332.42 6104.19 <.0001 M200906 1 33212939.832 33212939.832 574.65 <.0001 M200907 1 4843449459.1 4843449459.1 83801.1 <.0001 M200908 1 3566946760.5 3566946760.5 61715.2 <.0001 M200909 1 500005900.69 500005900.69 8651.08 <.0001 M200910 1 469450935.32 469450935.32 8122.42 <.0001

110

Dependent Variable: kwh Source DF Type I SS Mean Square F Value Pr > F M200911 1 42954310.225 42954310.225 743.19 <.0001 M200912 1 1588454570.6 1588454570.6 27483.4 <.0001 M201001 1 777176991.74 777176991.74 13446.7 <.0001 M201002 1 482438045.12 482438045.12 8347.12 <.0001 M201003 1 232675624.1 232675624.1 4025.74 <.0001 M201004 1 1435350933.3 1435350933.3 24834.4 <.0001 M201005 1 1333034998.5 1333034998.5 23064.1 <.0001 M201006 1 969487.19693 969487.19693 16.77 <.0001 M201007 1 2511383699.1 2511383699.1 43451.8 <.0001 M201008 1 712584732.48 712584732.48 12329.1 <.0001 M201009 1 10745045.768 10745045.768 185.91 <.0001 M201010 1 478196442.35 478196442.35 8273.73 <.0001 M201011 1 126831848.39 126831848.39 2194.44 <.0001 M201012 1 880791359.39 880791359.39 15239.4 <.0001 M201101 1 834461782.41 834461782.41 14437.8 <.0001 M201102 1 155497153.89 155497153.89 2690.40 <.0001 M201103 1 34233008.114 34233008.114 592.30 <.0001 M201104 1 1767536800.1 1767536800.1 30581.8 <.0001 M201105 1 2880891420.3 2880891420.3 49845.1 <.0001 M201106 1 654560868.23 654560868.23 11325.2 <.0001 M201107 1 191423859.56 191423859.56 3312.01 <.0001 M201108 1 317756533.79 317756533.79 5497.81 <.0001 cdd 1 983253382.81 983253382.81 17012.2 <.0001 hdd 1 249776223.93 249776223.93 4321.62 <.0001 cdd*post 1 6821226.7055 6821226.7055 118.02 <.0001 hdd*post 1 329619.51208 329619.51208 5.70 0.0169 cdd*Treatment 1 21236899.635 21236899.635 367.44 <.0001 hdd*Treatment 1 1805927.0177 1805927.0177 31.25 <.0001 Other_dum 1 173776326.93 173776326.93 3006.67 <.0001 OtherAfter_dum 1 4786483.3513 4786483.3513 82.82 <.0001 part*y2008 1 681884.06702 681884.06702 11.80 0.0006 part*y2009 1 16805413.414 16805413.414 290.77 <.0001 part*y2010 1 30495188.327 30495188.327 527.63 <.0001 part*y2011 1 31147696.613 31147696.613 538.92 <.0001 Source DF Type III SS Mean Square F Value Pr > F M200704 1 10114166.3 10114166.3 174.99 <.0001 M200705 1 12041273.3 12041273.3 208.34 <.0001 M200706 1 5454309.5 5454309.5 94.37 <.0001 M200707 1 7742773.6 7742773.6 133.97 <.0001 M200708 1 4325006.8 4325006.8 74.83 <.0001 M200709 1 11727279.7 11727279.7 202.90 <.0001 M200710 1 50019421.5 50019421.5 865.43 <.0001 M200711 1 21447458.7 21447458.7 371.08 <.0001 M200712 1 19910994.8 19910994.8 344.50 <.0001

111

Dependent Variable: kwh Source DF Type III SS Mean Square F Value Pr > F M200801 1 17088700.8 17088700.8 295.67 <.0001 M200802 1 8568087.5 8568087.5 148.24 <.0001 M200803 1 16525359.3 16525359.3 285.92 <.0001 M200804 1 5140448.0 5140448.0 88.94 <.0001 M200805 1 82360704.7 82360704.7 1425.00 <.0001 M200806 1 118791354.9 118791354.9 2055.32 <.0001 M200807 1 188121029.8 188121029.8 3254.86 <.0001 M200808 1 144321047.7 144321047.7 2497.04 <.0001 M200809 1 49191674.2 49191674.2 851.11 <.0001 M200810 1 60929198.5 60929198.5 1054.19 <.0001 M200811 1 42406722.2 42406722.2 733.72 <.0001 M200812 1 42356692.6 42356692.6 732.85 <.0001 M200901 1 34146523.4 34146523.4 590.80 <.0001 M200902 1 3592303.0 3592303.0 62.15 <.0001 M200903 1 21156710.6 21156710.6 366.05 <.0001 M200904 1 11043458.1 11043458.1 191.07 <.0001 M200905 1 85497286.2 85497286.2 1479.27 <.0001 M200906 1 147095487.3 147095487.3 2545.04 <.0001 M200907 1 320258981.3 320258981.3 5541.11 <.0001 M200908 1 155277158.3 155277158.3 2686.60 <.0001 M200909 1 31568943.8 31568943.8 546.20 <.0001 M200910 1 61683351.5 61683351.5 1067.24 <.0001 M200911 1 18701688.4 18701688.4 323.58 <.0001 M200912 1 33666447.4 33666447.4 582.50 <.0001 M201001 1 27114136.2 27114136.2 469.13 <.0001 M201002 1 701466.0 701466.0 12.14 0.0005 M201003 1 11652034.4 11652034.4 201.60 <.0001 M201004 1 8252955.2 8252955.2 142.79 <.0001 M201005 1 23071688.3 23071688.3 399.19 <.0001 M201006 1 59074649.9 59074649.9 1022.11 <.0001 M201007 1 337642981.7 337642981.7 5841.88 <.0001 M201008 1 166739293.9 166739293.9 2884.92 <.0001 M201009 1 15369826.9 15369826.9 265.93 <.0001 M201010 1 30680699.2 30680699.2 530.84 <.0001 M201011 1 18459852.5 18459852.5 319.39 <.0001 M201012 1 35282958.9 35282958.9 610.46 <.0001 M201101 1 19145126.0 19145126.0 331.25 <.0001 M201102 1 126581.2 126581.2 2.19 0.1389 M201103 1 11055896.8 11055896.8 191.29 <.0001 M201104 1 8141563.5 8141563.5 140.86 <.0001 M201105 1 25063141.3 25063141.3 433.64 <.0001 M201106 1 82439557.9 82439557.9 1426.37 <.0001 M201107 1 239414235.3 239414235.3 4142.33 <.0001 M201108 1 80245050.1 80245050.1 1388.40 <.0001 cdd 1 331591695.2 331591695.2 5737.18 <.0001 hdd 1 43067995.1 43067995.1 745.16 <.0001 cdd*post 1 6737912.0 6737912.0 116.58 <.0001

112

Dependent Variable: kwh Source DF Type III SS Mean Square F Value Pr > F hdd*post 1 375060.4 375060.4 6.49 0.0109 cdd*Treatment 1 11813148.9 11813148.9 204.39 <.0001 hdd*Treatment 1 915777.1 915777.1 15.84 <.0001 Other_dum 1 17935135.5 17935135.5 310.31 <.0001 OtherAfter_dum 1 4696128.6 4696128.6 81.25 <.0001 part*y2008 1 28994935.2 28994935.2 501.67 <.0001 part*y2009 1 58479582.0 58479582.0 1011.81 <.0001 part*y2010 1 54019778.5 54019778.5 934.65 <.0001 part*y2011 1 31147696.6 31147696.6 538.92 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200704 60.2051084 4.55114543 13.23 <.0001 51.2850245 69.1251923 M200705 62.4089088 4.32377212 14.43 <.0001 53.9344686 70.8833491 M200706 59.2040892 6.09444594 9.71 <.0001 47.2591910 71.1489874 M200707 87.1478046 7.52940373 11.57 <.0001 72.3904399 101.9051692 M200708 68.3983449 7.90687020 8.65 <.0001 52.9011592 83.8955305 M200709 72.4760941 5.08801699 14.24 <.0001 62.5037610 82.4484273 M200710 120.4130880 4.09314167 29.42 <.0001 112.3906753 128.4355007 M200711 127.6764755 6.62789134 19.26 <.0001 114.6860432 140.6669079 M200712 210.5274765 11.34265492 18.56 <.0001 188.2962745 232.7586785 M200801 200.6829177 11.67100617 17.19 <.0001 177.8081589 223.5576765 M200802 103.4399983 8.49569617 12.18 <.0001 86.7887347 120.0912620 M200803 105.6824045 6.24999464 16.91 <.0001 93.4326363 117.9321727 M200804 45.6422026 4.83970092 9.43 <.0001 36.1565602 55.1278451 M200805 84.0760860 2.22722900 37.75 <.0001 79.7107960 88.4413759 M200806 84.1855867 1.85693944 45.34 <.0001 80.5460512 87.8251223 M200807 128.4397531 2.25129913 57.05 <.0001 124.0272865 132.8522196 M200808 114.6733736 2.29482814 49.97 <.0001 110.1755917 119.1711555 M200809 53.7824415 1.84351691 29.17 <.0001 50.1692136 57.3956694 M200810 87.2927231 2.68854756 32.47 <.0001 82.0232651 92.5621811 M200811 106.6667492 3.93789491 27.09 <.0001 98.9486146 114.3848838 M200812 165.7389523 6.12231940 27.07 <.0001 153.7394230 177.7384815 M200901 144.1262926 5.92955884 24.31 <.0001 132.5045672 155.7480179 M200902 38.3539940 4.86492928 7.88 <.0001 28.8189049 47.8890831 M200903 81.8943503 4.28037959 19.13 <.0001 73.5049579 90.2837427 M200904 44.7953946 3.24065828 13.82 <.0001 38.4438192 51.1469701 M200905 90.0396345 2.34104614 38.46 <.0001 85.4512669 94.6280020 M200906 96.8632084 1.92004581 50.45 <.0001 93.0999866 100.6264302 M200907 154.9889137 2.08210386 74.44 <.0001 150.9080639 159.0697636 M200908 108.2140410 2.08776803 51.83 <.0001 104.1220895 112.3059924 M200909 43.5663169 1.86411729 23.37 <.0001 39.9127130 47.2199208 M200910 92.5564442 2.83318605 32.67 <.0001 87.0034998 98.1093885 M200911 83.6769733 4.65176717 17.99 <.0001 74.5596744 92.7942722 M200912 147.2940067 6.10293642 24.13 <.0001 135.3324674 159.2555460

113

Dependent Variable: kwh Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M201001 123.9641938 5.72335637 21.66 <.0001 112.7466180 135.1817697 M201002 16.4989914 4.73594333 3.48 0.0005 7.2167101 25.7812726 M201003 64.2078864 4.52209738 14.20 <.0001 55.3447357 73.0710372 M201004 46.0023120 3.84970392 11.95 <.0001 38.4570286 53.5475954 M201005 59.5747136 2.98177377 19.98 <.0001 53.7305426 65.4188846 M201006 61.7071276 1.93013269 31.97 <.0001 57.9241358 65.4901193 M201007 145.7667872 1.90713806 76.43 <.0001 142.0288641 149.5047102 M201008 100.2258449 1.86600654 53.71 <.0001 96.5685382 103.8831516 M201009 31.4190373 1.92668537 16.31 <.0001 27.6428022 35.1952724 M201010 59.4838259 2.58177673 23.04 <.0001 54.4236349 64.5440168 M201011 79.6790634 4.45843584 17.87 <.0001 70.9406870 88.4174398 M201012 141.7991713 5.73909757 24.71 <.0001 130.5507433 153.0475993 M201101 108.0928418 5.93909449 18.20 <.0001 96.4524269 119.7332567 M201102 7.5798172 5.12184546 1.48 0.1389 -2.4588186 17.6184529 M201103 65.7522500 4.75407517 13.83 <.0001 56.4344310 75.0700689 M201104 43.2042248 3.64019564 11.87 <.0001 36.0695703 50.3388794 M201105 66.3955040 3.18840392 20.82 <.0001 60.1463452 72.6446628 M201106 76.1934286 2.01744686 37.77 <.0001 72.2393042 80.1475530 M201107 121.7029244 1.89094303 64.36 <.0001 117.9967430 125.4091058 M201108 87.3307299 2.34374511 37.26 <.0001 82.7370725 91.9243873 cdd 1.5577350 0.02056573 75.74 <.0001 1.5174269 1.5980431 hdd 0.5594801 0.02049556 27.30 <.0001 0.5193095 0.5996506 cdd*post -0.2519788 0.02333747 -10.80 <.0001 -0.2977194 -0.2062382 hdd*post 0.0584768 0.02295541 2.55 0.0109 0.0134850 0.1034685 cdd*Treatment -0.0438004 0.00306371 -14.30 <.0001 -0.0498052 -0.0377957 hdd*Treatment -0.0083918 0.00210820 -3.98 <.0001 -0.0125238 -0.0042598 Other_dum -30.1233083 1.71002614 -17.62 <.0001 -33.4748990 -26.7717176 OtherAfter_dum -16.3081243 1.80919942 -9.01 <.0001 -19.8540911 -12.7621575 part*y2008 -17.2778828 0.77140397 -22.40 <.0001 -18.7898073 -15.7659583 part*y2009 -22.9905649 0.72276968 -31.81 <.0001 -24.4071679 -21.5739619 part*y2010 -22.5439845 0.73740604 -30.57 <.0001 -23.9892742 -21.0986948 part*y2011 -19.8242042 0.85395544 -23.21 <.0001 -21.4979266 -18.1504818

114

APPENDIX L: SYNERGY EFFECT FROM JOINT PROGRAM

PARTICIPATION

Dependent Variable: ln_kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 139184 1390876.960 9.993 168.17 <.0001 Error 5.24E6 311173.752 0.059 Corrected Total 5.38E6 1702050.711 R-Square Coeff Var Root MSE ln_kwh Mean 0.817177 3.622516 0.243769 6.729272 Source DF Type I SS Mean Square F Value Pr > F cont_acc 139021 1292033.850 9.294 156.40 <.0001 M200704 1 2720.471 2720.471 45781.2 <.0001 M200705 1 644.861 644.861 10852.0 <.0001 M200706 1 832.406 832.406 14008.1 <.0001 M200707 1 5179.179 5179.179 87157.4 <.0001 M200708 1 6073.289 6073.289 102204 <.0001 M200709 1 193.912 193.912 3263.23 <.0001 M200710 1 1641.602 1641.602 27625.6 <.0001 M200711 1 248.753 248.753 4186.13 <.0001 M200712 1 1969.262 1969.262 33139.6 <.0001 M200801 1 1912.221 1912.221 32179.7 <.0001 M200802 1 74.765 74.765 1258.18 <.0001 M200803 1 730.402 730.402 12291.5 <.0001 M200804 1 2595.656 2595.656 43680.8 <.0001 M200805 1 227.704 227.704 3831.90 <.0001 M200806 1 1053.948 1053.948 17736.3 <.0001 M200807 1 5830.556 5830.556 98119.0 <.0001 M200808 1 6013.732 6013.732 101202 <.0001 M200809 1 400.757 400.757 6744.11 <.0001 M200810 1 1074.531 1074.531 18082.7 <.0001 M200811 1 399.908 399.908 6729.82 <.0001 M200812 1 1719.153 1719.153 28930.6 <.0001 M200901 1 1048.724 1048.724 17648.4 <.0001 M200902 1 449.638 449.638 7566.69 <.0001 M200903 1 430.237 430.237 7240.22 <.0001 M200904 1 2115.887 2115.887 35607.0 <.0001 M200905 1 330.263 330.263 5557.81 <.0001 M200906 1 96.148 96.148 1618.02 <.0001 M200907 1 5161.613 5161.613 86861.8 <.0001 M200908 1 3941.179 3941.179 66323.8 <.0001 M200909 1 752.456 752.456 12662.7 <.0001 M200910 1 570.037 570.037 9592.82 <.0001 M200911 1 90.467 90.467 1522.41 <.0001 M200912 1 2540.450 2540.450 42751.8 <.0001 M201001 1 1352.645 1352.645 22762.9 <.0001 M201002 1 1281.942 1281.942 21573.1 <.0001 M201003 1 500.617 500.617 8424.60 <.0001 M201004 1 3313.996 3313.996 55769.3 <.0001 M201005 1 2774.519 2774.519 46690.8 <.0001 M201006 1 67.764 67.764 1140.35 <.0001 M201007 1 5117.464 5117.464 86118.8 <.0001 M201008 1 1647.543 1647.543 27725.5 <.0001 M201009 1 3.381 3.381 56.90 <.0001

115

M201010 1 740.883 740.883 12467.9 <.0001 M201011 1 359.141 359.141 6043.77 <.0001 M201012 1 1093.297 1093.297 18398.5 <.0001 M201101 1 898.829 898.829 15125.9 <.0001 M201102 1 980.766 980.766 16504.7 <.0001 M201103 1 364.017 364.017 6125.84 <.0001 M201104 1 5534.787 5534.787 93141.7 <.0001 M201105 1 7457.890 7457.890 125504 <.0001 M201106 1 912.037 912.037 15348.1 <.0001 M201107 1 529.261 529.261 8906.63 <.0001 M201108 1 1494.686 1494.686 25153.2 <.0001 M200704*cdd 1 0.028 0.028 0.47 0.4951 M200705*cdd 1 40.938 40.938 688.92 <.0001 M200706*cdd 1 54.690 54.690 920.34 <.0001 M200707*cdd 1 21.696 21.696 365.11 <.0001 M200708*cdd 1 73.319 73.319 1233.85 <.0001 M200709*cdd 1 212.267 212.267 3572.11 <.0001 M200710*cdd 1 9.964 9.964 167.68 <.0001 M200711*cdd 1 3.102 3.102 52.20 <.0001 M200712*cdd 1 0.025 0.025 0.42 0.5180 M200803*cdd 1 4.557 4.557 76.68 <.0001 M200804*cdd 1 50.869 50.869 856.04 <.0001 M200805*cdd 1 59.685 59.685 1004.40 <.0001 M200806*cdd 1 63.529 63.529 1069.09 <.0001 M200807*cdd 1 27.887 27.887 469.29 <.0001 M200808*cdd 1 51.262 51.262 862.66 <.0001 M200809*cdd 1 1.116 1.116 18.78 <.0001 M200810*cdd 1 55.790 55.790 938.85 <.0001 M200811*cdd 1 12.389 12.389 208.48 <.0001 M200812*cdd 1 0.116 0.116 1.95 0.1631 M200901*cdd 1 0.206 0.206 3.47 0.0627 M200902*cdd 1 0.030 0.030 0.50 0.4800 M200903*cdd 1 0.000 0.000 0.00 0.9470 M200904*cdd 1 27.932 27.932 470.06 <.0001

116

Dependent Variable: ln_kwh Source DF Type I SS Mean Square F Value Pr > F M200905*cdd 1 24.876 24.876 418.62 <.0001 M200906*cdd 1 14.749 14.749 248.20 <.0001 M200907*cdd 1 49.395 49.395 831.24 <.0001 M200908*cdd 1 64.540 64.540 1086.10 <.0001 M200909*cdd 1 20.001 20.001 336.59 <.0001 M200910*cdd 1 113.361 113.361 1907.69 <.0001 M200911*cdd 1 38.251 38.251 643.70 <.0001 M200912*cdd 1 0.005 0.005 0.09 0.7682 M201001*cdd 1 0.006 0.006 0.10 0.7549 M201002*cdd 1 0.091 0.091 1.52 0.2172 M201003*cdd 1 0.428 0.428 7.20 0.0073 M201004*cdd 1 0.036 0.036 0.61 0.4345 M201005*cdd 1 27.769 27.769 467.31 <.0001 M201006*cdd 1 22.805 22.805 383.78 <.0001 M201007*cdd 1 288.935 288.935 4862.31 <.0001 M201008*cdd 1 410.493 410.493 6907.94 <.0001 M201009*cdd 1 4.086 4.086 68.76 <.0001 M201010*cdd 1 114.686 114.686 1929.98 <.0001 M201011*cdd 1 5.975 5.975 100.55 <.0001 M201012*cdd 1 2.380 2.380 40.05 <.0001 M201101*cdd 1 0.236 0.236 3.98 0.0461 M201102*cdd 1 0.287 0.287 4.84 0.0278 M201103*cdd 1 1.979 1.979 33.30 <.0001 M201104*cdd 1 10.834 10.834 182.33 <.0001 M201105*cdd 1 92.950 92.950 1564.20 <.0001 M201106*cdd 1 230.158 230.158 3873.20 <.0001 M201107*cdd 1 18.403 18.403 309.69 <.0001 M201108*cdd 1 45.856 45.856 771.68 <.0001 M200704*hdd 1 5.675 5.675 95.51 <.0001 M200705*hdd 1 0.588 0.588 9.89 0.0017 M200706*hdd 1 11.946 11.946 201.04 <.0001 M200707*hdd 1 6.229 6.229 104.83 <.0001 M200708*hdd 1 1.351 1.351 22.74 <.0001 M200709*hdd 1 0.714 0.714 12.01 0.0005 M200710*hdd 1 4.723 4.723 79.49 <.0001 M200711*hdd 1 1.522 1.522 25.61 <.0001 M200712*hdd 1 31.284 31.284 526.46 <.0001 M200801*hdd 1 1.936 1.936 32.58 <.0001 M200802*hdd 1 0.006 0.006 0.10 0.7503 M200803*hdd 1 9.080 9.080 152.80 <.0001 M200804*hdd 1 1.911 1.911 32.16 <.0001 M200805*hdd 1 2.262 2.262 38.07 <.0001 M200806*hdd 1 13.508 13.508 227.32 <.0001 M200807*hdd 1 0.052 0.052 0.87 0.3507 M200808*hdd 1 0.054 0.054 0.90 0.3427 M200809*hdd 1 3.409 3.409 57.37 <.0001 M200810*hdd 1 0.357 0.357 6.02 0.0142

117

Dependent Variable: ln_kwh Source DF Type I SS Mean Square F Value Pr > F M200811*hdd 1 5.071 5.071 85.34 <.0001 M200812*hdd 1 37.521 37.521 631.41 <.0001 M200901*hdd 1 22.109 22.109 372.06 <.0001 M200902*hdd 1 2.389 2.389 40.21 <.0001 M200903*hdd 1 18.587 18.587 312.79 <.0001 M200904*hdd 1 1.244 1.244 20.93 <.0001 M200905*hdd 1 9.013 9.013 151.68 <.0001 M200906*hdd 1 0.605 0.605 10.17 0.0014 M200907*hdd 1 0.001 0.001 0.01 0.9159 M200908*hdd 1 0.165 0.165 2.78 0.0953 M200909*hdd 1 4.727 4.727 79.55 <.0001 M200910*hdd 1 53.324 53.324 897.36 <.0001 M200911*hdd 1 0.010 0.010 0.17 0.6812 M200912*hdd 1 49.169 49.169 827.43 <.0001 M201001*hdd 1 0.004 0.004 0.07 0.7929 M201002*hdd 1 27.638 27.638 465.11 <.0001 M201003*hdd 1 22.381 22.381 376.64 <.0001 M201004*hdd 1 27.670 27.670 465.64 <.0001 M201005*hdd 1 6.879 6.879 115.77 <.0001 M201006*hdd 1 10.308 10.308 173.47 <.0001 M201007*hdd 1 0.532 0.532 8.96 0.0028 M201008*hdd 1 0.235 0.235 3.95 0.0469 M201009*hdd 1 11.537 11.537 194.15 <.0001 M201010*hdd 1 8.954 8.954 150.68 <.0001 M201011*hdd 1 9.155 9.155 154.06 <.0001 M201012*hdd 1 73.741 73.741 1240.94 <.0001 M201101*hdd 1 37.915 37.915 638.05 <.0001 M201102*hdd 1 0.244 0.244 4.11 0.0427 M201103*hdd 1 31.055 31.055 522.60 <.0001 M201104*hdd 1 50.332 50.332 847.00 <.0001 M201105*hdd 1 24.582 24.582 413.67 <.0001 M201106*hdd 1 42.701 42.701 718.59 <.0001 M201107*hdd 1 1.347 1.347 22.67 <.0001 M201108*hdd 1 0.102 0.102 1.71 0.1906 cdd*Treatment 1 3.343 3.343 56.25 <.0001 hdd*Treatment 1 0.123 0.123 2.07 0.1498 Other_dum 1 135.457 135.457 2279.52 <.0001 OtherAfter_dum 1 1.543 1.543 25.96 <.0001 part 1 149.257 149.257 2511.76 <.0001 OtherAfter_dum*part 1 0.936 0.936 15.76 <.0001 Source DF Type III SS Mean Square F Value Pr > F M200704 1 1.1665850 1.1665850 19.63 <.0001 M200705 1 65.0992946 65.0992946 1095.52 <.0001 M200706 1 23.5413813 23.5413813 396.16 <.0001

118

Dependent Variable: ln_kwh Source DF Type III SS Mean Square F Value Pr > F M200707 1 0.0073267 0.0073267 0.12 0.7255 M200708 1 6.4691491 6.4691491 108.87 <.0001 M200709 1 150.0317853 150.0317853 2524.80 <.0001 M200710 1 9.8709303 9.8709303 166.11 <.0001 M200711 1 5.7676469 5.7676469 97.06 <.0001 M200712 1 11.2880040 11.2880040 189.96 <.0001 M200801 1 0.2281191 0.2281191 3.84 0.0501 M200802 1 1.1954723 1.1954723 20.12 <.0001 M200803 1 18.0557562 18.0557562 303.85 <.0001 M200804 1 43.2879475 43.2879475 728.47 <.0001 M200805 1 34.7835710 34.7835710 585.35 <.0001 M200806 1 35.0769775 35.0769775 590.29 <.0001 M200807 1 9.0077332 9.0077332 151.59 <.0001 M200808 1 12.4400423 12.4400423 209.35 <.0001 M200809 1 1.0291871 1.0291871 17.32 <.0001 M200810 1 157.3457864 157.3457864 2647.88 <.0001 M200811 1 13.3353057 13.3353057 224.41 <.0001 M200812 1 12.0231381 12.0231381 202.33 <.0001 M200901 1 18.4047170 18.4047170 309.72 <.0001 M200902 1 1.2343697 1.2343697 20.77 <.0001 M200903 1 23.5434818 23.5434818 396.20 <.0001 M200904 1 24.2675729 24.2675729 408.38 <.0001 M200905 1 27.4118373 27.4118373 461.30 <.0001 M200906 1 10.9413815 10.9413815 184.13 <.0001 M200907 1 12.2699111 12.2699111 206.48 <.0001 M200908 1 28.3265143 28.3265143 476.69 <.0001 M200909 1 1.4456455 1.4456455 24.33 <.0001 M200910 1 225.7080537 225.7080537 3798.31 <.0001 M200911 1 1.8142202 1.8142202 30.53 <.0001 M200912 1 24.8527796 24.8527796 418.23 <.0001 M201001 1 1.6402232 1.6402232 27.60 <.0001 M201002 1 53.5126861 53.5126861 900.53 <.0001 M201003 1 35.7762547 35.7762547 602.06 <.0001 M201004 1 86.6160393 86.6160393 1457.61 <.0001 M201005 1 1.1845267 1.1845267 19.93 <.0001 M201006 1 29.0369736 29.0369736 488.65 <.0001 M201007 1 152.4256742 152.4256742 2565.08 <.0001 M201008 1 307.7339495 307.7339495 5178.68 <.0001 M201009 1 15.7441779 15.7441779 264.95 <.0001 M201010 1 149.4935588 149.4935588 2515.74 <.0001 M201011 1 24.9396627 24.9396627 419.70 <.0001 M201012 1 53.6096916 53.6096916 902.17 <.0001 M201101 1 23.6170521 23.6170521 397.44 <.0001 M201102 1 4.6619084 4.6619084 78.45 <.0001 M201103 1 42.9984498 42.9984498 723.60 <.0001 M201104 1 133.3202865 133.3202865 2243.57 <.0001 M201105 1 82.0627385 82.0627385 1380.99 <.0001

119

Dependent Variable: ln_kwh Source DF Type III SS Mean Square F Value Pr > F M201106 1 154.5191757 154.5191757 2600.31 <.0001 M201107 1 2.7294307 2.7294307 45.93 <.0001 M201108 1 30.7428595 30.7428595 517.35 <.0001 M200704*cdd 1 5.2366341 5.2366341 88.12 <.0001 M200705*cdd 1 49.5453975 49.5453975 833.77 <.0001 M200706*cdd 1 45.9690034 45.9690034 773.59 <.0001 M200707*cdd 1 3.7550101 3.7550101 63.19 <.0001 M200708*cdd 1 29.1257203 29.1257203 490.14 <.0001 M200709*cdd 1 219.0503790 219.0503790 3686.27 <.0001 M200710*cdd 1 0.0000009 0.0000009 0.00 0.9969 M200711*cdd 1 7.8547234 7.8547234 132.18 <.0001 M200712*cdd 1 0.0000006 0.0000006 0.00 0.9974 M200803*cdd 1 9.7342499 9.7342499 163.81 <.0001 M200804*cdd 1 50.9523716 50.9523716 857.45 <.0001 M200805*cdd 1 41.7236687 41.7236687 702.14 <.0001 M200806*cdd 1 73.8777241 73.8777241 1243.25 <.0001 M200807*cdd 1 26.5284118 26.5284118 446.43 <.0001 M200808*cdd 1 43.8736486 43.8736486 738.32 <.0001 M200809*cdd 1 2.1386182 2.1386182 35.99 <.0001 M200810*cdd 1 36.1579083 36.1579083 608.48 <.0001 M200811*cdd 1 3.8275056 3.8275056 64.41 <.0001 M200812*cdd 1 0.4939963 0.4939963 8.31 0.0039 M200901*cdd 1 0.5223860 0.5223860 8.79 0.0030 M200902*cdd 1 0.0079652 0.0079652 0.13 0.7143 M200903*cdd 1 0.0436348 0.0436348 0.73 0.3915 M200904*cdd 1 22.6146643 22.6146643 380.57 <.0001 M200905*cdd 1 24.5789968 24.5789968 413.63 <.0001 M200906*cdd 1 16.2914872 16.2914872 274.16 <.0001 M200907*cdd 1 48.7330345 48.7330345 820.10 <.0001 M200908*cdd 1 62.8820095 62.8820095 1058.20 <.0001 M200909*cdd 1 0.0249735 0.0249735 0.42 0.5168 M200910*cdd 1 123.9141815 123.9141815 2085.28 <.0001 M200911*cdd 1 35.3720434 35.3720434 595.26 <.0001 M200912*cdd 1 0.0193031 0.0193031 0.32 0.5687 M201001*cdd 1 0.2609916 0.2609916 4.39 0.0361 M201002*cdd 1 0.0595225 0.0595225 1.00 0.3169 M201003*cdd 1 0.3160573 0.3160573 5.32 0.0211 M201004*cdd 1 7.0255250 7.0255250 118.23 <.0001 M201005*cdd 1 5.3337275 5.3337275 89.76 <.0001 M201006*cdd 1 32.0885469 32.0885469 540.00 <.0001 M201007*cdd 1 266.3642159 266.3642159 4482.49 <.0001 M201008*cdd 1 390.8226347 390.8226347 6576.93 <.0001 M201009*cdd 1 11.2638694 11.2638694 189.55 <.0001 M201010*cdd 1 105.4096837 105.4096837 1773.88 <.0001 M201011*cdd 1 1.1168086 1.1168086 18.79 <.0001

120

Dependent Variable: ln_kwh Source DF Type III SS Mean Square F Value Pr > F M201012*cdd 1 0.3606254 0.3606254 6.07 0.0138 M201101*cdd 1 0.1649763 0.1649763 2.78 0.0957 M201102*cdd 1 0.1607850 0.1607850 2.71 0.1000 M201103*cdd 1 1.8505139 1.8505139 31.14 <.0001 M201104*cdd 1 0.2866324 0.2866324 4.82 0.0281 M201105*cdd 1 75.5007760 75.5007760 1270.56 <.0001 M201106*cdd 1 218.9039368 218.9039368 3683.81 <.0001 M201107*cdd 1 18.9298818 18.9298818 318.56 <.0001 M201108*cdd 1 47.9182167 47.9182167 806.39 <.0001 M200704*hdd 1 7.6007054 7.6007054 127.91 <.0001 M200705*hdd 1 0.7469804 0.7469804 12.57 0.0004 M200706*hdd 1 11.7042573 11.7042573 196.96 <.0001 M200707*hdd 1 6.2270473 6.2270473 104.79 <.0001 M200708*hdd 1 1.1007420 1.1007420 18.52 <.0001 M200709*hdd 1 0.4536285 0.4536285 7.63 0.0057 M200710*hdd 1 5.5144155 5.5144155 92.80 <.0001 M200711*hdd 1 1.4356371 1.4356371 24.16 <.0001 M200712*hdd 1 26.0463474 26.0463474 438.32 <.0001 M200801*hdd 1 0.8880106 0.8880106 14.94 0.0001 M200802*hdd 1 0.4132054 0.4132054 6.95 0.0084 M200803*hdd 1 7.0340692 7.0340692 118.37 <.0001 M200804*hdd 1 1.7329051 1.7329051 29.16 <.0001 M200805*hdd 1 1.5271694 1.5271694 25.70 <.0001 M200806*hdd 1 14.7338962 14.7338962 247.95 <.0001 M200807*hdd 1 0.0499336 0.0499336 0.84 0.3593 M200808*hdd 1 0.0373152 0.0373152 0.63 0.4281 M200809*hdd 1 4.7172402 4.7172402 79.38 <.0001 M200810*hdd 1 0.4720037 0.4720037 7.94 0.0048 M200811*hdd 1 4.6388940 4.6388940 78.07 <.0001 M200812*hdd 1 31.4533480 31.4533480 529.31 <.0001 M200901*hdd 1 25.7085831 25.7085831 432.63 <.0001 M200902*hdd 1 2.7040322 2.7040322 45.50 <.0001 M200903*hdd 1 18.5810962 18.5810962 312.69 <.0001 M200904*hdd 1 0.9268575 0.9268575 15.60 <.0001 M200905*hdd 1 8.9716398 8.9716398 150.98 <.0001 M200906*hdd 1 1.0386353 1.0386353 17.48 <.0001 M200907*hdd 1 0.0001089 0.0001089 0.00 0.9659 M200908*hdd 1 0.1556168 0.1556168 2.62 0.1056 M200909*hdd 1 4.2079202 4.2079202 70.81 <.0001 M200910*hdd 1 53.9326184 53.9326184 907.60 <.0001 M200911*hdd 1 0.0128195 0.0128195 0.22 0.6423 M200912*hdd 1 40.0589439 40.0589439 674.13 <.0001 M201001*hdd 1 0.5497193 0.5497193 9.25 0.0024 M201002*hdd 1 31.0295190 31.0295190 522.18 <.0001 M201003*hdd 1 25.8332917 25.8332917 434.73 <.0001 M201004*hdd 1 26.8685526 26.8685526 452.16 <.0001 M201005*hdd 1 7.8668354 7.8668354 132.39 <.0001

121

Dependent Variable: ln_kwh Source DF Type III SS Mean Square F Value Pr > F M201006*hdd 1 12.5538966 12.5538966 211.26 <.0001 M201007*hdd 1 0.8014872 0.8014872 13.49 0.0002 M201008*hdd 1 0.0592459 0.0592459 1.00 0.3180 M201009*hdd 1 11.6761304 11.6761304 196.49 <.0001 M201010*hdd 1 9.4100194 9.4100194 158.36 <.0001 M201011*hdd 1 7.3741814 7.3741814 124.10 <.0001 M201012*hdd 1 74.9774813 74.9774813 1261.75 <.0001 M201101*hdd 1 33.8093185 33.8093185 568.96 <.0001 M201102*hdd 1 0.0255237 0.0255237 0.43 0.5122 M201103*hdd 1 32.3508451 32.3508451 544.41 <.0001 M201104*hdd 1 48.9584037 48.9584037 823.89 <.0001 M201105*hdd 1 24.2868439 24.2868439 408.71 <.0001 M201106*hdd 1 41.5166676 41.5166676 698.66 <.0001 M201107*hdd 1 1.4765491 1.4765491 24.85 <.0001 M201108*hdd 1 0.1012236 0.1012236 1.70 0.1918 cdd*Treatment 1 0.0069423 0.0069423 0.12 0.7325 hdd*Treatment 1 0.6046275 0.6046275 10.17 0.0014 Other_dum 1 20.6672282 20.6672282 347.80 <.0001 OtherAfter_dum 1 0.7630625 0.7630625 12.84 0.0003 part 1 141.9969538 141.9969538 2389.58 <.0001 OtherAfter_dum*part 1 0.9362157 0.9362157 15.76 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200704 0.1102142737 0.02487471 4.43 <.0001 0.0614607293 0.1589678181 M200705 -.3090103558 0.00933605 -33.10 <.0001 -.3273086851 -.2907120266 M200706 -.3393873162 0.01705132 -19.90 <.0001 -.3728072958 -.3059673365 M200707 -.0114351509 0.03256616 -0.35 0.7255 -.0752636601 0.0523933583 M200708 -.2484328641 0.02381024 -10.43 <.0001 -.2951000929 -.2017656354 M200709 -.3427709285 0.00682167 -50.25 <.0001 -.3561411588 -.3294006982 M200710 -.2829321271 0.02195238 -12.89 <.0001 -.3259580075 -.2399062468 M200711 -.1454301489 0.01476160 -9.85 <.0001 -.1743623624 -.1164979355 M200712 -.2871544840 0.02083461 -13.78 <.0001 -.3279895790 -.2463193890 M200801 -.1458277090 0.07442821 -1.96 0.0501 -.2917043568 0.0000489388 M200802 -.0984786806 0.02195588 -4.49 <.0001 -.1415114221 -.0554459391 M200803 -.2960633622 0.01698459 -17.43 <.0001 -.3293525631 -.2627741613 M200804 -.3480390092 0.01289504 -26.99 <.0001 -.3733128340 -.3227651844 M200805 -.2787927859 0.01152319 -24.19 <.0001 -.3013778326 -.2562077393 M200806 -.2933524615 0.01207416 -24.30 <.0001 -.3170173902 -.2696875329 M200807 -.3801829126 0.03087900 -12.31 <.0001 -.4407046493 -.3196611760 M200808 -.3064670687 0.02118123 -14.47 <.0001 -.3479815322 -.2649526051 M200809 -.1200529875 0.02884723 -4.16 <.0001 -.1765925364 -.0635134386 M200810 -.3189134774 0.00619760 -51.46 <.0001 -.3310605565 -.3067663983 M200811 -.2633667729 0.01758077 -14.98 <.0001 -.2978244533 -.2289090926 M200812 -.2265324565 0.01592575 -14.22 <.0001 -.2577463584 -.1953185545 M200901 -.5655449127 0.03213519 -17.60 <.0001 -.6285287436 -.5025610817 M200902 0.1880453700 0.04125897 4.56 <.0001 0.1071792567 0.2689114832 M200903 -.7677559034 0.03857147 -19.90 <.0001 -.8433546098 -.6921571970 M200904 -.3427221734 0.01695928 -20.21 <.0001 -.3759617571 -.3094825897 M200905 -.4607469598 0.02145220 -21.48 <.0001 -.5027925013 -.4187014183 M200906 -.1649614832 0.01215696 -13.57 <.0001 -.1887886835 -.1411342829 M200907 -.2582118956 0.01796941 -14.37 <.0001 -.2934313061 -.2229924851 M200908 -.4361963899 0.01997854 -21.83 <.0001 -.4753536272 -.3970391526 M200909 0.1297813538 0.02631236 4.93 <.0001 0.0782100705 0.1813526372 M200910 -.3431774984 0.00556831 -61.63 <.0001 -.3540911936 -.3322638032 M200911 -.0729935278 0.01321045 -5.53 <.0001 -.0988855457 -.0471015100 M200912 -.4201011193 0.02054209 -20.45 <.0001 -.4603628926 -.3798393460 M201001 0.1635106333 0.03112239 5.25 <.0001 0.1025118652 0.2245094013 M201002 -.5564880630 0.01854410 -30.01 <.0001 -.5928338454 -.5201422807 M201003 -.6049654095 0.02465537 -24.54 <.0001 -.6532890507 -.5566417683 M201004 -.4099409230 0.01073743 -38.18 <.0001 -.4309859098 -.3888959361 M201005 -.0649656235 0.01455089 -4.46 <.0001 -.0934848495 -.0364463976 M201006 -.4557958812 0.02061926 -22.11 <.0001 -.4962088975 -.4153828649 M201007 -.5859361222 0.01156910 -50.65 <.0001 -.6086111462 -.5632610983

122

M201008 -.7214501128 0.01002529 -71.96 <.0001 -.7410993250 -.7018009005 M201009 -.2132970117 0.01310398 -16.28 <.0001 -.2389803411 -.1876136824 M201010 -.4028541255 0.00803184 -50.16 <.0001 -.4185962419 -.3871120091 M201011 -.1827841392 0.00892219 -20.49 <.0001 -.2002713186 -.1652969598 M201012 -.4041254149 0.01345466 -30.04 <.0001 -.4304960782 -.3777547516 M201101 -.2745528799 0.01377183 -19.94 <.0001 -.3015451682 -.2475605916 M201102 -.1317726388 0.01487722 -8.86 <.0001 -.1609314570 -.1026138207 M201103 -.4854444970 0.01804644 -26.90 <.0001 -.5208148771 -.4500741169 M201104 -.5876916561 0.01240737 -47.37 <.0001 -.6120096615 -.5633736508 M201105 -.5667730071 0.01525157 -37.16 <.0001 -.5966655389 -.5368804753 M201106 -.6109448380 0.01198089 -50.99 <.0001 -.6344269606 -.5874627153 M201107 -.1078454604 0.01591271 -6.78 <.0001 -.1390338087 -.0766571121 M201108 -.6478739013 0.02848372 -22.75 <.0001 -.7037009796 -.5920468230 cdd*Treatment 0.0000009508 0.00000278 0.34 0.7325 -.0000045014 0.0000064031 Treatment*hdd 0.0000060332 0.00000189 3.19 0.0014 0.0000023261 0.0000097403 M200704*cdd -.0017249849 0.00018375 -9.39 <.0001 -.0020851368 -.0013648330 M200705*cdd 0.0018211823 0.00006307 28.88 <.0001 0.0016975653 0.0019447993 M200706*cdd 0.0019351834 0.00006958 27.81 <.0001 0.0017988143 0.0020715526 M200707*cdd 0.0007967955 0.00010024 7.95 <.0001 0.0006003383 0.0009932526 M200708*cdd 0.0014733685 0.00006655 22.14 <.0001 0.0013429318 0.0016038051 M200709*cdd 0.0022183596 0.00003654 60.71 <.0001 0.0021467475 0.0022899718 M200710*cdd 0.0000016364 0.00042691 0.00 0.9969 -.0008350938 0.0008383666 M200711*cdd 0.0062560670 0.00054414 11.50 <.0001 0.0051895630 0.0073225711 M200712*cdd -.0001892202 0.05777335 -0.00 0.9974 -.1134229381 0.1130444976 M200803*cdd 0.0042205542 0.00032976 12.80 <.0001 0.0035742383 0.0048668700 M200804*cdd 0.0027388068 0.00009353 29.28 <.0001 0.0025554885 0.0029221250 M200805*cdd 0.0017349176 0.00006547 26.50 <.0001 0.0016065918 0.0018632435 M200806*cdd 0.0013808789 0.00003916 35.26 <.0001 0.0013041206 0.0014576372 M200807*cdd 0.0017333745 0.00008204 21.13 <.0001 0.0015725830 0.0018941661 M200808*cdd 0.0015020966 0.00005528 27.17 <.0001 0.0013937482 0.0016104450 M200809*cdd 0.0006626763 0.00011046 6.00 <.0001 0.0004461746 0.0008791781 M200810*cdd 0.0021293166 0.00008632 24.67 <.0001 0.0019601303 0.0022985029 M200811*cdd 0.0022861027 0.00028485 8.03 <.0001 0.0017278069 0.0028443985 M200812*cdd -.0045018649 0.00156138 -2.88 0.0039 -.0075621164 -.0014416133 M200901*cdd -.0044338294 0.00149541 -2.96 0.0030 -.0073647884 -.0015028704 M200902*cdd 0.0005721557 0.00156277 0.37 0.7143 -.0024908207 0.0036351321 M200903*cdd -.0002144822 0.00025030 -0.86 0.3915 -.0007050524 0.0002760880 M200904*cdd 0.0028224844 0.00014468 19.51 <.0001 0.0025389126 0.0031060562 M200905*cdd 0.0023714775 0.00011660 20.34 <.0001 0.0021429368 0.0026000182 M200906*cdd 0.0008911767 0.00005382 16.56 <.0001 0.0007856870 0.0009966664 M200907*cdd 0.0014857239 0.00005188 28.64 <.0001 0.0013840399 0.0015874080 M200908*cdd 0.0018722821 0.00005756 32.53 <.0001 0.0017594755 0.0019850887 M200909*cdd -.0000513636 0.00007923 -0.65 0.5168 -.0002066530 0.0001039258 M200910*cdd 0.0013587498 0.00002975 45.66 <.0001 0.0013004314 0.0014170681 M200911*cdd 0.0067696908 0.00027747 24.40 <.0001 0.0062258581 0.0073135235 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200912*cdd -.0008300944 0.00145644 -0.57 0.5687 -.0036846640 0.0020244751 M201001*cdd 0.0039195158 0.00187024 2.10 0.0361 0.0002539104 0.0075851212 M201002*cdd 0.0016622546 0.00166087 1.00 0.3169 -.0015929904 0.0049174996 M201003*cdd 0.0025701021 0.00111441 2.31 0.0211 0.0003858941 0.0047543100 M201004*cdd 0.0058215454 0.00053540 10.87 <.0001 0.0047721832 0.0068709077 M201005*cdd 0.0006538943 0.00006902 9.47 <.0001 0.0005186189 0.0007891696 M201006*cdd 0.0020512802 0.00008827 23.24 <.0001 0.0018782681 0.0022242923 M201007*cdd 0.0025892285 0.00003867 66.95 <.0001 0.0025134303 0.0026650267 M201008*cdd 0.0031843560 0.00003927 81.10 <.0001 0.0031073972 0.0032613148 M201009*cdd 0.0008147732 0.00005918 13.77 <.0001 0.0006987835 0.0009307629 M201010*cdd 0.0020155528 0.00004786 42.12 <.0001 0.0019217576 0.0021093479 M201011*cdd 0.0007244313 0.00016710 4.34 <.0001 0.0003969139 0.0010519487 M201012*cdd -.0027396769 0.00111211 -2.46 0.0138 -.0049193821 -.0005599717 M201101*cdd -.0045721396 0.00274402 -1.67 0.0957 -.0099503187 0.0008060395 M201102*cdd -.0046023045 0.00279789 -1.64 0.1000 -.0100860732 0.0008814642 M201103*cdd 0.0047588863 0.00085278 5.58 <.0001 0.0030874659 0.0064303067 M201104*cdd 0.0004061224 0.00018492 2.20 0.0281 0.0000436951 0.0007685496 M201105*cdd 0.0037142844 0.00010420 35.64 <.0001 0.0035100514 0.0039185174 M201106*cdd 0.0026063669 0.00004294 60.69 <.0001 0.0025222011 0.0026905326 M201107*cdd 0.0009341710 0.00005234 17.85 <.0001 0.0008315872 0.0010367548 M201108*cdd 0.0024894788 0.00008767 28.40 <.0001 0.0023176544 0.0026613031

123

M200704*hdd -.0016313735 0.00014425 -11.31 <.0001 -.0019140912 -.0013486558 M200705*hdd 0.0003030945 0.00008549 3.55 0.0004 0.0001355424 0.0004706467 M200706*hdd -.0122222482 0.00087088 -14.03 <.0001 -.0139291389 -.0105153575 M200707*hdd 0.0919556694 0.00898288 10.24 <.0001 0.0743495367 0.1095618021 M200708*hdd 0.0053567817 0.00124463 4.30 <.0001 0.0029173552 0.0077962082 M200709*hdd -.0003555169 0.00012867 -2.76 0.0057 -.0006077120 -.0001033218 M200710*hdd 0.0012913271 0.00013405 9.63 <.0001 0.0010285950 0.0015540591 M200711*hdd 0.0002543682 0.00005175 4.92 <.0001 0.0001529380 0.0003557983 M200712*hdd 0.0008246295 0.00003939 20.94 <.0001 0.0007474305 0.0009018286 M200801*hdd 0.0005277780 0.00013653 3.87 0.0001 0.0002601887 0.0007953673 M200802*hdd 0.0001521557 0.00005770 2.64 0.0084 0.0000390637 0.0002652477 M200803*hdd 0.0007154303 0.00006576 10.88 <.0001 0.0005865488 0.0008443118 M200804*hdd 0.0004276712 0.00007920 5.40 <.0001 0.0002724506 0.0005828917 M200805*hdd -.0004255571 0.00008394 -5.07 <.0001 -.0005900855 -.0002610287 M200806*hdd 0.0043414578 0.00027571 15.75 <.0001 0.0038010727 0.0048818428 M200807*hdd -.0015231649 0.00166161 -0.92 0.3593 -.0047798580 0.0017335281 M200808*hdd -.0008718858 0.00110026 -0.79 0.4281 -.0030283539 0.0012845822 M200809*hdd 0.0016189890 0.00018171 8.91 <.0001 0.0012628443 0.0019751336 M200810*hdd 0.0002756787 0.00009782 2.82 0.0048 0.0000839633 0.0004673941 M200811*hdd 0.0006648192 0.00007524 8.84 <.0001 0.0005173427 0.0008122957 M200812*hdd 0.0006795373 0.00002954 23.01 <.0001 0.0006216469 0.0007374277 M200901*hdd 0.0012953402 0.00006228 20.80 <.0001 0.0011732808 0.0014173997 M200902*hdd -.0007269713 0.00010777 -6.75 <.0001 -.0009381927 -.0005157499 M200903*hdd 0.0022754699 0.00012868 17.68 <.0001 0.0020232601 0.0025276797 M200904*hdd 0.0003064631 0.00007760 3.95 <.0001 0.0001543740 0.0004585523 M200905*hdd 0.0019977232 0.00016258 12.29 <.0001 0.0016790643 0.0023163820 M200906*hdd 0.0038146355 0.00091243 4.18 <.0001 0.0020263039 0.0056029670 M200907*hdd -.0000823297 0.00192298 -0.04 0.9659 -.0038513007 0.0036866414 M200908*hdd -.0027886552 0.00172324 -1.62 0.1056 -.0061661380 0.0005888275 M200909*hdd -.0024616541 0.00029253 -8.42 <.0001 -.0030350041 -.0018883041 M200910*hdd 0.0014927167 0.00004955 30.13 <.0001 0.0013956036 0.0015898299 M200911*hdd -.0000178372 0.00003840 -0.46 0.6423 -.0000931063 0.0000574319 M200912*hdd 0.0009937290 0.00003827 25.96 <.0001 0.0009187146 0.0010687434 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M201001*hdd -.0001926264 0.00006333 -3.04 0.0024 -.0003167551 -.0000684978 M201002*hdd 0.0011644658 0.00005096 22.85 <.0001 0.0010645887 0.0012643429 M201003*hdd 0.0015408443 0.00007390 20.85 <.0001 0.0013960020 0.0016856866 M201004*hdd 0.0008791288 0.00004134 21.26 <.0001 0.0007980967 0.0009601608 M201005*hdd -.0011734363 0.00010199 -11.51 <.0001 -.0013733240 -.0009735486 M201006*hdd 0.0014738650 0.00010140 14.53 <.0001 0.0012751206 0.0016726095 M201007*hdd 0.0025837118 0.00070352 3.67 0.0002 0.0012048450 0.0039625786 M201008*hdd -.0004541677 0.00045485 -1.00 0.3180 -.0013456522 0.0004373167 M201009*hdd 0.0042334728 0.00030201 14.02 <.0001 0.0036415380 0.0048254075 M201010*hdd 0.0010373587 0.00008244 12.58 <.0001 0.0008757889 0.0011989285 M201011*hdd 0.0003121327 0.00002802 11.14 <.0001 0.0002572154 0.0003670499 M201012*hdd 0.0009701869 0.00002731 35.52 <.0001 0.0009166545 0.0010237193 M201101*hdd 0.0006319406 0.00002649 23.85 <.0001 0.0005800146 0.0006838666 M201102*hdd 0.0000232180 0.00003543 0.66 0.5122 -.0000462173 0.0000926533 M201103*hdd 0.0010856175 0.00004653 23.33 <.0001 0.0009944247 0.0011768103 M201104*hdd 0.0019107758 0.00006657 28.70 <.0001 0.0017803022 0.0020412494 M201105*hdd 0.0020261087 0.00010022 20.22 <.0001 0.0018296805 0.0022225369 M201106*hdd 0.0026673799 0.00010091 26.43 <.0001 0.0024695918 0.0028651680 M201107*hdd -.0040093039 0.00080431 -4.98 <.0001 -.0055857225 -.0024328852 M201108*hdd -.0137408876 0.01052815 -1.31 0.1918 -.0343756933 0.0068939182 Other_dum -.0289467155 0.00155216 -18.65 <.0001 -.0319888942 -.0259045368 OtherAfter_dum -.0064096818 0.00178869 -3.58 0.0003 -.0099154481 -.0029039156 part -.0234772988 0.00048027 -48.88 <.0001 -.0244186142 -.0225359835 OtherAfter_dum*part -.0064169773 0.00161667 -3.97 <.0001 -.0095855894 -.0032483652

124

APPENDIX M: BILLING ANALYSIS ON SURVEY SAMPLE

Dependent Variable: kwh Sum of Source DF Squares Mean Square F Value Pr > F Model 783 5608615439 7162983 156.63 <.0001 Error 27002 1234837034 45731 Corrected Total 27785 6843452472 R-Square Coeff Var Root MSE kwh Mean 0.819559 22.30065 213.8488 958.9356 Source DF Type I SS Mean Square F Value Pr > F cont_acc 721 5177261002 7180667 157.02 <.0001 M200704 1 7754725 7754725 169.57 <.0001 M200705 1 1768200 1768200 38.66 <.0001 M200706 1 2887604 2887604 63.14 <.0001 M200707 1 19597755 19597755 428.54 <.0001 M200708 1 25340606 25340606 554.12 <.0001 M200709 1 237625 237625 5.20 0.0226 M200710 1 4945123 4945123 108.13 <.0001 M200711 1 742628 742628 16.24 <.0001 M200712 1 10769986 10769986 235.51 <.0001 M200801 1 10528997 10528997 230.24 <.0001 M200802 1 47836 47836 1.05 0.3064 M200803 1 1624534 1624534 35.52 <.0001 M200804 1 6670969 6670969 145.87 <.0001 M200805 1 722293 722293 15.79 <.0001 M200806 1 2880106 2880106 62.98 <.0001 M200807 1 20635615 20635615 451.24 <.0001 M200808 1 22866795 22866795 500.02 <.0001 M200809 1 640803 640803 14.01 0.0002 M200810 1 3247099 3247099 71.00 <.0001 M200811 1 1160063 1160063 25.37 <.0001 M200812 1 9536656 9536656 208.54 <.0001 M200901 1 5732321 5732321 125.35 <.0001 M200902 1 1411476 1411476 30.86 <.0001 M200903 1 1464401 1464401 32.02 <.0001 M200904 1 5853352 5853352 127.99 <.0001 M200905 1 1318958 1318958 28.84 <.0001 M200906 1 223796 223796 4.89 0.0270 M200907 1 20839176 20839176 455.69 <.0001 M200908 1 15640143 15640143 342.00 <.0001 M200909 1 2527725 2527725 55.27 <.0001 M200910 1 2708376 2708376 59.22 <.0001

125

Dependent Variable: kwh Source DF Type I SS Mean Square F Value Pr > F M200911 1 588847 588847 12.88 0.0003 M200912 1 17253683 17253683 377.28 <.0001 M201001 1 7733018 7733018 169.10 <.0001 M201002 1 7293434 7293434 159.48 <.0001 M201003 1 2913477 2913477 63.71 <.0001 M201004 1 15174486 15174486 331.82 <.0001 M201005 1 13511218 13511218 295.45 <.0001 M201006 1 284830 284830 6.23 0.0126 M201007 1 28549322 28549322 624.28 <.0001 M201008 1 8295024 8295024 181.39 <.0001 M201009 1 6413 6413 0.14 0.7080 M201010 1 4574683 4574683 100.03 <.0001 M201011 1 2069484 2069484 45.25 <.0001 M201012 1 8905709 8905709 194.74 <.0001 M201101 1 7072351 7072351 154.65 <.0001 M201102 1 4359008 4359008 95.32 <.0001 M201103 1 1911754 1911754 41.80 <.0001 M201104 1 25888654 25888654 566.10 <.0001 M201105 1 32392436 32392436 708.32 <.0001 M201106 1 5152035 5152035 112.66 <.0001 M201107 1 3799261 3799261 83.08 <.0001 M201108 1 10218584 10218584 223.45 <.0001 cdd 1 4087545 4087545 89.38 <.0001 hdd 1 2130077 2130077 46.58 <.0001 cdd*post 1 2595340 2595340 56.75 <.0001 hdd*post 1 11795 11795 0.26 0.6116 cdd*Treatment 1 340252 340252 7.44 0.0064 hdd*Treatment 1 315005 315005 6.89 0.0087 Other_dum 1 32423 32423 0.71 0.3998 OtherAfter_dum 1 760821 760821 16.64 <.0001 part 1 807726 807726 17.66 <.0001 Source DF Type III SS Mean Square F Value Pr > F M200704 1 47397.118 47397.118 1.04 0.3087 M200705 1 379630.544 379630.544 8.30 0.0040 M200706 1 1155549.075 1155549.075 25.27 <.0001 M200707 1 1876046.612 1876046.612 41.02 <.0001 M200708 1 1668000.459 1668000.459 36.47 <.0001 M200709 1 774265.606 774265.606 16.93 <.0001 M200710 1 376454.187 376454.187 8.23 0.0041 M200711 1 173884.071 173884.071 3.80 0.0512 M200712 1 265396.119 265396.119 5.80 0.0160 M200801 1 195852.954 195852.954 4.28 0.0385 M200802 1 34398.394 34398.394 0.75 0.3858 M200803 1 115688.641 115688.641 2.53 0.1117

126

Dependent Variable: kwh Source DF Type III SS Mean Square F Value Pr > F M200804 1 24176.263 24176.263 0.53 0.4672 M200805 1 933084.194 933084.194 20.40 <.0001 M200806 1 2279099.469 2279099.469 49.84 <.0001 M200807 1 3061810.115 3061810.115 66.95 <.0001 M200808 1 2855397.714 2855397.714 62.44 <.0001 M200809 1 921218.618 921218.618 20.14 <.0001 M200810 1 464520.297 464520.297 10.16 0.0014 M200811 1 156429.001 156429.001 3.42 0.0644 M200812 1 144950.306 144950.306 3.17 0.0750 M200901 1 89387.119 89387.119 1.95 0.1621 M200902 1 16745.819 16745.819 0.37 0.5451 M200903 1 21600.346 21600.346 0.47 0.4919 M200904 1 16547.594 16547.594 0.36 0.5475 M200905 1 627246.165 627246.165 13.72 0.0002 M200906 1 2028741.107 2028741.107 44.36 <.0001 M200907 1 5147244.967 5147244.967 112.55 <.0001 M200908 1 2954144.075 2954144.075 64.60 <.0001 M200909 1 892401.539 892401.539 19.51 <.0001 M200910 1 411936.648 411936.648 9.01 0.0027 M200911 1 11670.605 11670.605 0.26 0.6134 M200912 1 107381.044 107381.044 2.35 0.1254 M201001 1 56760.709 56760.709 1.24 0.2653 M201002 1 91822.915 91822.915 2.01 0.1565 M201003 1 4.194 4.194 0.00 0.9924 M201004 1 6435.821 6435.821 0.14 0.7076 M201005 1 18251.345 18251.345 0.40 0.5276 M201006 1 779261.953 779261.953 17.04 <.0001 M201007 1 5182361.492 5182361.492 113.32 <.0001 M201008 1 2467739.760 2467739.760 53.96 <.0001 M201009 1 106229.733 106229.733 2.32 0.1275 M201010 1 154482.880 154482.880 3.38 0.0661 M201011 1 1350.561 1350.561 0.03 0.8636 M201012 1 82330.635 82330.635 1.80 0.1797 M201101 1 10841.705 10841.705 0.24 0.6263 M201102 1 146204.382 146204.382 3.20 0.0738 M201103 1 5790.558 5790.558 0.13 0.7220 M201104 1 28258.193 28258.193 0.62 0.4318 M201105 1 21038.488 21038.488 0.46 0.4976 M201106 1 1061713.787 1061713.787 23.22 <.0001 M201107 1 6452459.966 6452459.966 141.09 <.0001 M201108 1 3126518.191 3126518.191 68.37 <.0001 cdd 1 5863411.848 5863411.848 128.21 <.0001 hdd 1 2060558.189 2060558.189 45.06 <.0001 cdd*post 1 1552269.927 1552269.927 33.94 <.0001 hdd*post 1 108988.817 108988.817 2.38 0.1227 cdd*Treatment 1 1810.843 1810.843 0.04 0.8423 hdd*Treatment 1 286215.083 286215.083 6.26 0.0124 Other_dum 1 735549.610 735549.610 16.08 <.0001 OtherAfter_dum 1 777228.068 777228.068 17.00 <.0001 part 1 807726.020 807726.020 17.66 <.0001 Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M200704 30.9246997 30.37640526 1.02 0.3087 -28.6146294 90.4640288 M200705 63.7309160 22.11955795 2.88 0.0040 20.3754356 107.0863964 M200706 88.6922315 17.64404843 5.03 <.0001 54.1089818 123.2754812 M200707 128.9162898 20.12763936 6.40 <.0001 89.4650732 168.3675065 M200708 128.7472718 21.31801721 6.04 <.0001 86.9628529 170.5316908 M200709 77.7523934 18.89625056 4.11 <.0001 40.7147626 114.7900242 M200710 87.2227701 30.40049573 2.87 0.0041 27.6362224 146.8093178 M200711 75.5359489 38.73741590 1.95 0.0512 -0.3913946 151.4632923 M200712 137.8408841 57.21865207 2.41 0.0160 25.6893596 249.9924086

127

M200801 121.1795008 58.55593941 2.07 0.0385 6.4068238 235.9521778 M200802 39.5226866 45.57054598 0.87 0.3858 -49.7979460 128.8433193 M200803 59.7049058 37.53803322 1.59 0.1117 -13.8715854 133.2813970 M200804 21.7767977 29.95065220 0.73 0.4672 -36.9280333 80.4816288 M200805 85.5267435 18.93426173 4.52 <.0001 48.4146088 122.6388781 M200806 101.3503146 14.35655759 7.06 <.0001 73.2107174 129.4899118 M200807 153.2779848 18.73257636 8.18 <.0001 116.5611639 189.9948056 M200808 152.0182612 19.23841609 7.90 <.0001 114.3099683 189.7265542 M200809 63.7851565 14.21167047 4.49 <.0001 35.9295456 91.6407674 M200810 76.4620485 23.99111529 3.19 0.0014 29.4382187 123.4858783 M200811 67.1235623 36.29302917 1.85 0.0644 -4.0126565 138.2597810 M200812 101.8948063 57.23337228 1.78 0.0750 -10.2855706 214.0751832 M200901 77.1588061 55.18929989 1.40 0.1621 -31.0150829 185.3326951 M200902 -27.3526663 45.20154992 -0.61 0.5451 -115.9500475 61.2447150 M200903 27.2065522 39.58676446 0.69 0.4919 -50.3855585 104.7986629 M200904 17.7503465 29.50844094 0.60 0.5475 -40.0877275 75.5884206 M200905 74.4585869 20.10493341 3.70 0.0002 35.0518751 113.8652987 M200906 101.1565958 15.18755094 6.66 <.0001 71.3882086 130.9249830 M200907 177.4493674 16.72606935 10.61 <.0001 144.6654043 210.2333305 M200908 135.0717426 16.80565807 8.04 <.0001 102.1317815 168.0117036 M200909 63.1795921 14.30222213 4.42 <.0001 35.1464952 91.2126889 M200910 72.4121147 24.12697486 3.00 0.0027 25.1219932 119.7022363 M200911 21.2875703 42.13919842 0.51 0.6134 -61.3074433 103.8825838 M200912 86.4778388 56.43491457 1.53 0.1254 -24.1375196 197.0931971 M201001 58.7601965 52.74316527 1.11 0.2653 -44.6191418 162.1395348 M201002 -60.8948452 42.97460060 -1.42 0.1565 -145.1272903 23.3375999 M201003 -0.3923133 40.96386830 -0.01 0.9924 -80.6836189 79.8989923 M201004 -12.9436417 34.50335713 -0.38 0.7076 -80.5720105 54.6847270 M201005 16.1858295 25.62090007 0.63 0.5276 -34.0324629 66.4041219 M201006 54.5539499 13.21573497 4.13 <.0001 28.6504242 80.4574756

128

Dependent Variable: kwh Standard Parameter Estimate Error t Value Pr > |t| 95% Confidence Limits M201007 139.4567274 13.10033719 10.65 <.0001 113.7793874 165.1340675 M201008 90.5846056 12.33137699 7.35 <.0001 66.4144674 114.7547439 M201009 20.0583173 13.16067938 1.52 0.1275 -5.7372966 45.8539312 M201010 38.0206723 20.68647393 1.84 0.0661 -2.5258891 78.5672337 M201011 6.9387616 40.37678414 0.17 0.8636 -72.2018286 86.0793518 M201012 70.4293243 52.49037140 1.34 0.1797 -32.4545250 173.3131735 M201101 26.7404576 54.91954194 0.49 0.6263 -80.9046919 134.3856070 M201102 -84.1750889 47.07715884 -1.79 0.0738 -176.4487609 8.0985831 M201103 -15.3455789 43.12508147 -0.36 0.7220 -99.8729743 69.1818166 M201104 -25.4013405 32.31404316 -0.79 0.4318 -88.7385404 37.9358594 M201105 18.8515364 27.79369850 0.68 0.4976 -35.6255536 73.3286264 M201106 66.6360148 13.82968041 4.82 <.0001 39.5291242 93.7429054 M201107 141.0201192 11.87203790 11.88 <.0001 117.7503094 164.2899290 M201108 108.4540893 13.11663122 8.27 <.0001 82.7448121 134.1633665 cdd 1.2781578 0.11287978 11.32 <.0001 1.0569076 1.4994081 hdd 0.6694905 0.09973759 6.71 <.0001 0.4739997 0.8649814 cdd*post -0.2191623 0.03761743 -5.83 <.0001 -0.2928944 -0.1454302 hdd*post 0.0317385 0.02055904 1.54 0.1227 -0.0085582 0.0720353 cdd*Treatment -0.0067675 0.03400888 -0.20 0.8423 -0.0734266 0.0598917 hdd*Treatment -0.0572224 0.02287317 -2.50 0.0124 -0.1020550 -0.0123898 Other_dum 56.8003924 14.16290062 4.01 <.0001 29.0403729 84.5604119 OtherAfter_dum -62.0335349 15.04731969 -4.12 <.0001 -91.5270616 -32.5400082 part -23.7316580 5.64680644 -4.20 <.0001 -34.7996914 -12.6636247

129

APPENDIX N: SAMPLE REPORT OF ONLINE SURVEY

APPENDIX O: SAMPLE REPORT OF MAILER SURVEY

APPENDIX P: PHONE INTERVIEW SCRIP

APPENDIX Q: SAMPLE ONSITE DATA COLLECTION FORM

SAMPLE FORM 1 SAMPLE FORM 2

APPENDIX R: SAMPLE ONSITE VERIFICATION FORM

SAMPLE FORM 1 SAMPLE FORM 2 SAMPLE FORM 3

APPENDIX S: PHONE INTERVIEW DISPOSITION REPORT

http://www.integralanalytics.com/ia/Portals/0/smud-files/Appendix-N.pdf

http://www.integralanalytics.com/ia/Portals/0/smud-files/Appendix-O.pdf

http://www.integralanalytics.com/ia/Portals/0/smud-files/Appendix-P.pdf

http://www.integralanalytics.com/ia/Portals/0/smud-files/Appendix-Q-1.jpg

http://www.integralanalytics.com/ia/Portals/0/smud-files/Appendix-Q-2.jpg

http://www.integralanalytics.com/ia/Portals/0/smud-files/Appendix-R-1.jpg



130

APPENDIX T: ONSITE RECRUITING SCRIPT

APPENDIX U: ESTIMATION PROCESS

APPENDIX V: ENGINEERING APPROACH SUMMARY

APPENDIX W: ENERGY SAVINGS DERIVED FROM THE SIMULATIONS

http://www.integralanalytics.com/ia/Portals/0/smud-files/Appendix-S.xls

http://www.integralanalytics.com/ia/Portals/0/smud-files/Appendix-T.doc

http://www.integralanalytics.com/ia/Portals/0/smud-files/Appendix-U.doc

http://www.integralanalytics.com/ia/Portals/0/smud-files/Appendix-V.xlsx

http://www.integralanalytics.com/ia/Portals/0/smud-files/Appendix-W.xlsx

Documents

Sacramento Municipal Utility District Home Energy Report ...€¦ · Sacramento Municipal Utility District Home Energy Report Program Program Years 2008-2011 Report Date November