PG&E’s Emerging Technologies Program ET12PGE3461
Small Commercial EMS Scaled Field Placement
ET Project Number: ET12PGE3461
Project Managers: Leo Carrillo and Mananya Chansanchai Pacific Gas and Electric Company Prepared By: Lisa Gartland, PhD, Peter Pollard, P.E., Magdalena Brum kW Engineering Inc. 287 17th Street, Suite 300 Oakland, CA 94612
Issued: September 30, 2015
Copyright, 2015, Pacific Gas and Electric Company. All rights reserved.
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PG&E’s Emerging Technologies Program ET12PGE3461
ACKNOWLEDGEMENTS
Pacific Gas and Electric Company’s Emerging Technologies Program is responsible for this project. It was developed as part of Pacific Gas and Electric Company’s Emerging Technology –Technology Assessment program under internal project number ET12PGE3461. kW Engineering conducted this technology evaluation for Pacific Gas and Electric Company with overall guidance and Leo Carrillo and Mananya Chansanchai. PG&E would like to thank Siemens Building Technologies for their participation and support of this project.
For more information on this project, contact Leo Carrillo ([email protected]).
LEGAL NOTICE
This report was prepared for Pacific Gas and Electric Company for use by its employees and agents. Neither Pacific Gas and Electric Company nor any of its employees and agents:
(1) makes any written or oral warranty, expressed or implied, including, but not limited to those concerning merchantability or fitness for a particular purpose;
(2) assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, process, method, or policy contained herein; or
(3) represents that its use would not infringe any privately owned rights, including, but not limited to, patents, trademarks, or copyrights.
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PG&E’s Emerging Technologies Program ET12PGE3461
ABBREVIATIONS AND ACRONYMS
CT Current transducer
EMS Energy management systems
HVAC Heating, ventilating and air conditioning
NF New Functionality
PCT Programmable Communicating Thermostats
ST Supporting Technology
TMY3 Typical Meteorological Year. The TMY3 data set contains data for 1020 locations for years 1991-2005.
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PG&E’s Emerging Technologies Program ET12PGE3461
FIGURES
Figure 1A. Example HVAC Energy vs Outside Air Temperature –
Occupied Hours .......................................................... 18
Figure 1B. Example HVAC Energy vs Outside Air Temperature –
Staff Hours ................................................................ 19
Figure 1C. Example HVAC Energy vs Outside Air Temperature –
Unoccupied Hours ...................................................... 19
Figure 2. Daily Average Monitored HVAC Use at Placer County Fast
Food Restaurant Facility, Pre & Post EMS ...................... 20
Figure 3. Daily Average Monitored HVAC Use at Stanislaus County
Assembly/Restaurant Facility, Pre & Post EMS ................ 21
Figure 4. Daily Average Monitored HVAC Use at Kern County Retail
Clinic Facility, Pre & Post EMS Hours ............................. 21
Figure 5A. Daily Average Monitored Lighting Use at Kern County
Retail Clinic Facility, Pre & Post EMS ............................. 28
Figure 5B. Daily Average Monitored Lighting Use at Alameda
County Retail Facility, Pre & Post EMS ........................... 28
Figure 5C. Daily Average Monitored Lighting Use at Nevada County
Assembly Facility, Pre & Post EMS ................................ 28
Figure 5D. Daily Average Monitored Lighting Use at Lake County
Assembly Facility, Pre & Post EMS ................................ 28
Figure 5E. Daily Average Monitored Lighting Use at Stanislaus
County Assembly/Restaurant Facility, Pre & Post EMS ..... 29
Figure 5F. Average Monitored Daily Lighting Use at Santa Clara
County Assembly/Restaurant Facility, Pre & Post EMS ..... 29
Figure 5G. Daily Average Monitored Lighting Use at South Santa
Clara County Sit Down Restaurant Facility, Pre & Post
EMS.......................................................................... 29
Figure 5H. Daily Average Monitored Lighting Use at San Joaquin
County Sit Down Restaurant Facility, Pre & Post EMS ...... 29
Figure 5I. Daily Average Monitored Lighting Use at Alameda County
Fast Food Restaurant Facility, Pre & Post EMS ................ 30
Figure 5J. Daily Average Monitored Lighting Use at Placer County
Fast Food Restaurant Facility, Pre & Post EMS ................ 30
Figure 5K. Daily Average Monitored Lighting Use at Merced County
Fast Food Restaurant Facility, Pre & Post EMS ................ 30
Figure 6A. Daily Average Monitored HVAC Use at Kern County
Retail Clinic Facility, Pre & Post EMS ............................. 34
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PG&E’s Emerging Technologies Program ET12PGE3461
Figure 6B. Daily Average Monitored HVAC Use at Alameda County
Retail Facility, Pre & Post EMS ...................................... 34
Figure 6C. Daily Average Monitored HVAC Use at Nevada County
Assembly Restaurant Facility, Pre & Post EMS ................ 34
Figure 6D. Daily Average Monitored HVAC Use at Lake County
Assembly Facility, Pre & Post EMS ................................ 34
Figure 6E. Daily Average Monitored HVAC Use at Stanislaus County
Assembly/Restaurant Facility, Pre & Post EMS ................ 35
Figure 6F. Daily Average Monitored HVAC Use at Santa Clara
County Assembly/Restaurant Facility, Pre & Post EMS ..... 35
Figure 6G. Daily Average Monitored HVAC Use at South Santa Clara
County Sit Down Restaurant Facility, Pre & Post EMS ...... 35
Figure 6H. Daily Average Monitored HVAC Use at San Joaquin
County Sit Down Restaurant Facility, Pre & Post EMS ...... 35
Figure 6I. Daily Average Monitored HVAC Use at Alameda County
Fast Food Restaurant Facility, Pre & Post EMS ................ 36
Figure 6J. Daily Average Monitored HVAC Use at Placer County Fast
Food Restaurant Facility, Pre & Post EMS ...................... 36
Figure 6K. Daily Average Monitored HVAC Use at Merced County
Fast Food Restaurant Facility, Pre & Post EMS ................ 36
Figure 7. Cooling and Heating Setpoints During Occupied and
Unoccupied Periods Pre- and Post- EMS Installation ........ 39
Figure D - 1A. Kern County Retail HVAC Energy vs Outside Air
Temperature – Occupied Hours .................................... 70
Figure D – 1B. Kern County Retail HVAC Energy vs Outside Air
Temperature – Unoccupied Hours ................................. 70
Figure D - 2A. Alameda County Retail HVAC Energy vs Outside Air
Temperature – Occupied Hours .................................... 70
Figure D – 2B. Alameda County Retail HVAC Energy vs Outside Air
Temperature – Unoccupied Hours ................................. 70
Figure D – 3A. Nevada County Assembly HVAC Energy vs Outside
Air Temperature – Occupied Hours ............................... 71
Figure D – 3B. Nevada County Assembly HVAC Energy vs Outside
Air Temperature – Unoccupied Hours ............................ 71
Figure D – 4A. Lake County Assembly HVAC Energy vs Outside Air
Temperature – Occupied Hours .................................... 71
Figure D – 4B. Lake County Assembly HVAC Energy vs Outside Air
Temperature – Unoccupied Hours ................................. 71
Figure D – 5A. Stanislaus County Assembly/Restaurant HVAC
Energy vs Outside Air Temperature – Occupied Hours ..... 72
Figure D – 5B. Stanislaus County Assembly/Restaurant HVAC
Energy vs Outside Air Temperature – Unoccupied Hours . 72
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PG&E’s Emerging Technologies Program ET12PGE3461
Figure D – 6A. Santa Clara County Assembly/Restaurant HVAC
Energy vs Outside Air Temperature – Occupied Hours ..... 72
Figure D – 6B. Santa Clara County Assembly/Restaurant HVAC
Energy vs Outside Air Temperature – Unoccupied Hours . 72
Figure D – 7A. Santa Clara County Sit Down Restaurant HVAC
Energy vs Outside Air Temperature – Occupied Hours ..... 73
Figure D – 7B. Santa Clara County Sit Down Restaurant HVAC
Energy vs Outside Air Temperature – Staff Hours ........... 73
Figure D – 7C. Santa Clara County Sit Down Restaurant HVAC
Energy vs Outside Air Temperature – Unoccupied Hours . 73
Figure D – 8A. San Joaquin County Sit Down Restaurant HVAC
Energy vs Outside Air Temperature – Occupied Hours ..... 74
Figure D – 8B. San Joaquin County Sit Down Restaurant HVAC
Energy vs Outside Air Temperature – Staff Hours ........... 74
Figure D – 8C. San Joaquin County Sit Down Restaurant HVAC
Energy vs Outside Air Temperature – Unoccupied Hours . 74
Figure D – 9A. Alameda County Fast Food Restaurant HVAC Energy
vs Outside Air Temperature – Occupied Hours ............... 75
Figure D – 9B. Alameda County Fast Food Restaurant HVAC Energy
vs Outside Air Temperature – Unoccupied Hours ............ 75
Figure D – 10A. Placer County Fast Food Restaurant HVAC Energy
vs Outside Air Temperature – Occupied Hours ............... 75
Figure D – 10B. Placer County Fast Food Restaurant HVAC Energy
vs Outside Air Temperature – Unoccupied Hours ............ 75
Figure D – 11A. Merced County Fast Food Restaurant HVAC Energy
vs Outside Air Temperature – Occupied Hours ............... 76
Figure D – 11B. Merced County Fast Food Restaurant HVAC Energy
vs Outside Air Temperature – Unoccupied Hours ............ 76
TABLES
Table 1. Electricity Savings from EMS Installation in Eleven Small
Commercial Facilities .................................................... 2
Table 2. Business Type and Square Footage Data for PG&E Service
Territory as of 1999 ...................................................... 6
Table 3. Installation Dates & Monitoring Periods for Eleven Projects .. 11
Table 4. Local Weather Data, Annual Weather Data, and Building
Energy Use Reference Data Used to Annualize and
Normalize Monitoring Results ....................................... 14
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PG&E’s Emerging Technologies Program ET12PGE3461
Table 6. CEUS Electricity Use per Floor Area Used to Normalize
Savings in the Facilities ............................................... 22
Table 7. Annual Lighting and HVAC Electricity Savings Due to EMS
Installation, in Percent ................................................ 24
Table 8. Lighting Electricity Savings From EMS Installation during
Occupied, Staff & Unoccupied Periods ........................... 25
Table 9. HVAC Electricity Savings From EMS Installation during
Occupied, Staff & Unoccupied Periods ........................... 32
Table 10. Extreme Outdoor Low and High Temperatures During
Each Facility’s Monitoring Period ................................... 33
Table 11. Weekly Operating Hour Comparison ............................... 38
Table 12. Comparison of Temperature Setpoints, Reported Pre-
Install and EMS Post-Install ......................................... 40
Table 13: Potential Information to Collect to Determine the
Eligibility and Verify Effective Operation of Supporting
Technologies ............................................................. 49
Table 14: Potential Information Needed to Determine the Eligibility
and Verify Effective Operation of New Functionalities ...... 50
Table E - A. CEUS Electricity Use per Floor Area Used to Normalize
Savings in the Facilities ............................................... 76
Table E - B. Total Building Electricity Savings Estimates Based on
Typical CEUS Data ...................................................... 77
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PG&E’s Emerging Technologies Program ET12PGE3461
CONTENTS
ABBREVIATIONS AND ACRONYMS _____________________________________________ II
FIGURES _______________________________________________________________ III
TABLES ________________________________________________________________ V
CONTENTS _____________________________________________________________VII
EXECUTIVE SUMMARY _____________________________________________________ 1
INTRODUCTION __________________________________________________________ 4
BACKGROUND __________________________________________________________ 5
ASSESSMENT OBJECTIVES __________________________________________________ 7
EMERGING TECHNOLOGY/PRODUCT _________________________________________ 7
DATA COLLECTION APPROACH _____________________________________________ 8
“Before” and “After” Surveys .................................................... 8
Data Loggers ........................................................................ 10
EMS Online Data ................................................................... 12
ENERGY SAVINGS ANALYSIS AND RESULTS ____________________________________ 13
Lighting Analysis ................................................................... 15
HVAC Analysis ...................................................................... 15
Lighting Monitoring Savings Results ......................................... 24
HVAC Monitoring Savings Results ............................................ 31
Qualifications of Evaluation Findings ........................................ 37
Pre and Post Occupant Behavior .............................................. 37
Survey Highlights .................................................................. 41
Pre-Installation Surveys .................................................... 41 Post-Installation Surveys................................................... 41
EVALUATIONS __________________________________________________________ 42
POTENTIAL FOR EFFICIENCY PROGRAM INCENTIVES ______________________________ 43
Introduction ......................................................................... 43
Definitions ............................................................................ 43
Custom Incentive Measures .................................................... 44
Deemed Incentive Measures ................................................... 45
Hybrid-Deemed incentive Measures ......................................... 46
Screening Projects for Eligibility and Verification ....................... 49
Supporting Technologies ................................................... 49
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PG&E’s Emerging Technologies Program ET12PGE3461
New Functionalities........................................................... 50
EMS Controls: Fixed Incentive and Functionality Incentives ....... 51
RECOMMENDATIONS ____________________________________________________ 52
Potential Next Steps .............................................................. 52
Comparison of Savings with Existing Modeling Results .......... 52 Expand and Develop Incentive Approach and Tools .............. 53
Considerations for Future Field Studies .................................... 53
APPENDICES ___________________________________________________________ 55
Appendix A: Pre-Installation Survey ........................................ 55
Appendix B: Post-Installation Survey ....................................... 62
Appendix C: Post-Installation Survey Responses ....................... 67
Appendix D: Graphs of HVAC Energy vs Outside Air Temperature 69
Appendix E: Estimates of Whole Building Savings Using CEUS Benchmark Data
........................................................................................... 76
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PG&E’s Emerging Technologies Program ET12PGE3461
EXECUTIVE SUMMARY This Emerging Technology report describes the data collection and analysis done to evaluate
the energy savings potential of an energy management system (EMS) with integrated HVAC
and lighting control functionality that is specially designed for small commercial facilities. As
part of the project, kW Engineering performed a retrofit isolation analysis of the EMS
installation in eleven facilities representing a variety of building types and climate zones in
PG&E’s service territory. The installation of an EMS provided an opportunity for customers to
re-examine, improve and enforce operating schedules and settings.
For a limited period of time prior to and immediately following system installation, energy
use of HVAC and some lighting systems was monitored at each site. Information from
participant surveys and online EMS control systems was also used to inform the energy
savings analysis. HVAC energy data was extrapolated to estimate annualized savings based
on typical weather where appropriate. However for most sites, the range of weather during
the monitoring periods (in winter) was quite limited. This likely resulted in lower measured
HVAC savings than had the monitoring periods occurred during the summer months, when
HVAC usage is typically higher across California climate zones.
As shown in Table 1 on the following page, the greatest savings came from reductions in
HVAC energy use, averaging 18% in nine buildings with confirmed savings. HVAC data for
two sites was excluded due to anomalies; further detail is provided in the report. Savings
from reduced lighting use occurred in six of eleven buildings, with average lighting savings
of 6% for all sites.
Using benchmark data from the California Commercial End Use Study, kW estimated that
the lighting and HVAC savings from the eleven buildings translated into an average building
savings of approximately 5%, or 20 kWh per square foot. Note, these building-level savings
are estimates only, not using whole building data. See Appendix E.
While savings on average were significant, there were large variations from building to
building, as is typical for controls measures. Lighting energy savings varied from a low of -
6% to a high of 36%. Annualized HVAC savings ranged from a low of 6% to a high of 31%.
Savings depended primarily on how well the HVAC and lighting was controlled before the
EMS installation. For these small commercial sites, lighting was already fairly well-controlled
in most sites, with staff shutting off lights at the end of the day. In this sample, there was
no discernible pattern of lighting savings by building type.
For HVAC, the EMS controls provided savings through tighter scheduling, more overnight
setbacks, and daytime setpoint enforcement. A clear example is two restaurant sites with
“staff-only” hours, which achieved annualized savings of 60% on average during those
hours through temperature setbacks implemented using the EMS. Of course HVAC savings
are also impacted by climate, with more extreme climates using more energy for HVAC, and
thus providing more opportunity for savings through better HVAC controls. However,
climate factors were outweighed by the baseline scheduling in determining HVAC savings
achieved.
Table 1 on the following page summarizes the monitored percent savings for lighting and
HVAC after installation of small commercial EMS at 11 facilities.
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PG&E’s Emerging Technologies Program ET12PGE3461
TABLE 1. ELECTRICITY SAVINGS FROM EMS INSTALLATION IN ELEVEN SMALL COMMERCIAL FACILITIES
The ability to make broad conclusions from this study were impacted by the small number
of sites monitored (a common challenge with technology assessments), the limited range of
weather conditions monitored (with installation occurring in winter at many sites), and some
site anomalies and logger issues. Future studies could address these limitations, thereby
increasing the size of the study population and range of monitored weather conditions, and
hopefully minimizing the relative impact of site anomalies and logger issues on study
results.
Notwithstanding these limitations, our study of the eleven participating facilities suggests
that the implementation of the EMS is an effective measure for saving energy in small
commercial buildings, and overall, the EMS functioned mostly as expected during the
assessment period.
LocationCalifornia
Climate ZoneBuilding Type Lighting %
Savings
HVAC %
Savings
Kern County 13 Retail 14% 6%
Alameda
County12 Retail 0% 20%
Nevada
County11 Assembly - 25%
Lake County 2 Assembly 36% 9%
Stanislaus
County12
Assembly /
Restaurant7% -
Santa Clara
County4
Assembly /
Restaurant-4% 31%
Santa Clara
County4
Sit Down
Restaurant-6% 19%
San Joaquin
County12
Sit Down
Restaurant8% 28%
Alameda
County12
Fast Food
Restaurant-5% 18%
Placer County 11Fast Food
Restaurant17% -
Merced
County12
Fast Food
Restaurant1% 11%
AVERAGE 6% 18%
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PG&E’s Emerging Technologies Program ET12PGE3461
This report also explores the suitability of using efficiency program incentives - including
custom, deemed and hybrid-deemed incentive approaches - to encourage the adoption of
control retrofits (such as small commercial EMS) to save energy. These controls retrofit
projects present challenges in establishing baseline conditions, verifying installed results,
and estimating savings. With the custom incentive approach, the costs of project-specific
savings analyses are high relative to the cost of the project itself. Deemed incentive
programs offer lower costs but provide no data on actual results achieved. This report
proposes that hybrid-deemed incentive programs for controls projects, such as small
commercial EMS, can be developed which are simpler, faster, and less costly than
customized incentives, while still providing reliable estimates of energy savings in
aggregate.
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PG&E’s Emerging Technologies Program ET12PGE3461
INTRODUCTION This report describes a retrofit isolation analysis performed by kW Engineering to evaluate
the energy savings from the installation of an energy management system (EMS) with
integrated HVAC and lighting control functionality in small commercial facilities throughout
PG&E’s service territory. A total of fifteen sites installed the EMS, which has the ability to
monitor and control lighting and HVAC systems, and to track the overall electrical
consumption and demand of a facility.
Eleven of the fifteen participating facilities were selected for the retrofit isolation study, for
which kW Engineering performed more intensive onsite monitoring of their EMS operation
and energy use at these facilities. Four types of information were collected and analyzed for
this evaluation:
“Before” surveys filled out by facility personnel to learn how lights and thermostats
were operated before the EMS was installed, and to obtain some basic information
about each building
Logged data for the subset of eleven facilities, installed to collect at least six weeks
of lighting and HVAC electricity use data, and some representative indoor
temperatures and lighting levels
EMS data for the subset of eleven facilities, with information about total facility
energy use, operating schedules, and thermostat settings obtained from online
accounts for each site
Weather data from nearby weather stations was collected to give us information
about outdoor temperatures
The collected data was used to analyze how each facility’s lighting and HVAC energy use
changed due to the use of the small commercial EMS.
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PG&E’s Emerging Technologies Program ET12PGE3461
BACKGROUND Energy management systems are in common use in large commercial facilities. These
complex systems not only control lighting and space temperatures throughout large
buildings, they also deliver heating and cooling to the conditioned space more efficiently by
implementing strategies like economizer cooling, and temperature and/or static pressure
resets when outdoor and indoor temperatures permit. These systems have traditionally
needed to be hard-wired in order to send digital signals to a central controller. This has
made them somewhat expensive and difficult to install in existing buildings, but their cost
has been offset by their ability to save energy. EMS technology has not traditionally been
cost-effective in small commercial buildings, largely due to the fact that simpler HVAC
systems in these facilities present fewer efficiency opportunities and that EMS products have
heretofore been designed and priced primarily for larger facilities.
The advent of new EMS systems specially designed for small commercial lighting and HVAC
systems could address the cost effectiveness challenge of small commercial installations and
lead to improved market penetration among small commercial buildings. These systems
combine new wireless thermostats with pilot duty controllers, clamp-on current transducers,
and online software, to produce a cost-effective control system that is easy to install and
operate. These systems allow commercial facilities to control lighting and space
temperatures, and to visualize their energy use over time.
The energy savings opportunity in small commercial buildings is significant, especially with
regards to EMS technology1. Small commercial buildings represent the majority of
commercial facilities in PG&E’s service territory. According to PG&E’s 1999 Commercial
Building Survey Report2, premises of 5,000 square feet or smaller comprise 75% of the
1,764,630 commercial buildings served by PG&E in 1997. The table below is extracted from
this report, and shows the breakdown of building sizes by business type and climate region.
1 Estimation of EMS Presence in Commercial Buildings in PG&E Territory and a Snapshot of
Technologies in EMIS Landscape, ET Project Number ET11PGE4221, 2012
(http://www.etcc-ca.com/reports/estimation-ems-presence-commercial-buildings-pge-
territory-and-snapshot-technologies-emis).
2 Pacific Gas and Electric Company, Commercial Building Survey Report, 1999.
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PG&E’s Emerging Technologies Program ET12PGE3461
TABLE 2. BUSINESS TYPE AND SQUARE FOOTAGE DATA FOR PG&E SERVICE TERRITORY AS OF 1999
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PG&E’s Emerging Technologies Program ET12PGE3461
ASSESSMENT OBJECTIVES The data collected in this study aims to evaluate the energy savings potential from the use
of new EMS systems that have been developed specifically for use in small commercial
facilities. Achieving energy savings depends on how well these new systems are
implemented. Savings also depend heavily on how well each facility was being controlled
before the EMS was installed, and on other types of equipment used in each facility. Our
data collection is focused on measuring energy use, as well as figuring out the lighting and
HVAC system control strategies before and after the EMS installation, and understanding
where energy is used in each facility.
Out of the fifteen facilities participating in the overall project, eleven were included in the
retrofit isolation study to determine how their new EMS settings, schedules and other
features affected lighting and HVAC energy use. None of the sites studied used the EMS to
control refrigeration systems.
EMERGING TECHNOLOGY/PRODUCT The particular EMS product studied in this report is sold to third party distributors and
channel partners by an original equipment manufacturer (OEM). The EMS has the following
features:
an onsite central panel that is a touchscreen user interface, master controller, and
internet gateway for the EMS,
thermostats, with wireless communication to the central touchscreen panel, to
control HVAC or refrigeration equipment,
an 8DO (eight digital output) pilot-duty controller module used to switch up to eight
lighting or ventilation circuits on and off,
a multi-phase meter to measure total facility energy consumption, with split-core
current transducers to measure current, and voltage connections,
a web application that allows owners or managers to review current systems, view
historical trends, and change schedules and settings.
Note that the EMS application does not directly control the HVAC unit operation. Instead,
the EMS thermostat makes calls for cooling and heating like a traditional thermostat, and
separate controls within each HVAC unit are used to operate fans, compressors, and
economizers.
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PG&E’s Emerging Technologies Program ET12PGE3461
DATA COLLECTION APPROACH We collected five sets of information to inform us about each facility, and the operation and
energy use of lights and HVAC equipment. We describe each data collection instrument or
data collection method below.
“BEFORE” AND “AFTER” SURVEYS Surveys are designed to identify how all forty buildings in the overall pilot project are being
controlled and operated both before and after the installation of the new EMS. The surveys
were administered online. The surveys are administered to the person with the most day-to-
day control of lighting and HVAC systems for each facility. This might be a general manager,
employee, or facility manager.
The “before” survey is designed to help us understand how the facility was operated prior to
the installation of its new EMS. The “before” survey has five sections:
1. Survey Intro & Screening lays out the reason for the survey, tries to make sure
the right person is being surveyed, and identifies who that person is.
2. General Business/Facility Info collects information about the type of business
including number of employees, operating hours, and information about the
building’s square footage, number of stories, and whether the facility shares a
building with other businesses. This information helps us understand the businesses
and facilities we’re studying, and allows us to compare them to the overall mix of
small commercial businesses in California.
3. Lighting Info asks how lights are currently being controlled (via switches, control
systems, etc.), who controls them, how many groups of lights there are, schedules
of use, if there are any occupancy sensors, dimmers, photocells or timers, and if
there are any current problems with lighting operation. This information will be
compared to the “after” survey info to see how lighting control changes.
4. HVAC Info asks about the type of thermostats or control systems, what
temperature settings and schedules are used, who can adjust the settings, whether
there are any current problems with comfort or control, and how often the systems
are serviced. This will be compared to “after” survey data to see how HVAC control
changes with the EMS.
5. Other Equipment asks the participant about other types of energy-using equipment
in the facility, including cooking, refrigeration, office, computer, or process
equipment. This helps us understand how much of the building’s energy loads are
not due to lighting or HVAC equipment.
Appendix A contains the actual online “before” survey used in this project.
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PG&E’s Emerging Technologies Program ET12PGE3461
The “after” survey follows a format similar to the “before” survey, but questions are
designed to elicit information about changes in the facility or its operation since the EMS
was installed. There are six sections in the “after” survey:
1. Survey Intro & Screening describes the reason for this second survey, tries to
make sure the person who operates the EMS is being surveyed, and identifies who
that person is.
2. General Business/Facility Info collects information about any changes to
operating hours, employees, customers, or cleaning schedules since the installation
of the EMS. This includes details regarding the facility and its use, including the type
of business, number of employees, and operating hours.
3. Lighting Info asks if any lighting equipment changes have been made since the
start of the project, and then collects information about how the EMS is used to
operate lights, i.e. how many groups of lights are controlled, what their schedules
are, and who can override them. We also ask about the pros and cons of the EMS for
lighting.
4. HVAC Info asks if any HVAC equipment changes have been made since the start of
the project, and then collects information about how the EMS is used to adjust space
temperatures, i.e. what settings are used and when are they adjusted. We also ask
about the pros and cons of the EMS for HVAC system control, and about the
frequency of system servicing.
5. Other Equipment asks the participant about other types of energy-using equipment
being monitored of controlled by the EMS, including cooking, refrigeration, office,
computer, or process equipment, and whether they’ve changed the way this
equipment is being controlled because of the information they’re getting from the
EMS.
6. Alarms & Notifications is a new survey section, not included in the “before”
survey, where we ask about any notices the EMS may have sent and what was done
in response.
Appendix B has a copy of the “after” online survey questions.
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PG&E’s Emerging Technologies Program ET12PGE3461
DATA LOGGERS Data loggers were installed at a subset of eleven project sites. The loggers were used to
record the energy use of lighting and HVAC equipment as well as some representative
indoor space temperatures, and lighting levels.
We used three types of data loggers in this project:
1. Current loggers recorded the current draw of various pieces of equipment in the
facility and use the current data plus spot typical measurements of voltage and
power factor to find energy use. Current loggers are current transducers (CTs) that
are connected to a data recording device. CTs are clamped around the wires of
particular circuits within an electrical panel. Current loggers were hooked up to a
representative sample of lighting and HVAC circuits in order to understand when this
equipment operates and how much energy it uses. Each measured circuit was also
spot-checked with a multimeter to measure the power factor of the equipment, and
the voltage.
2. Temperature loggers recorded the indoor temperature and humidity at various
locations in the facility. These loggers were placed above or below the “before”
thermostat locations.
3. Light loggers recorded the levels of illumination in the building. Loggers were
placed in locations that are representative of each group of lighting being controlled.
Loggers were set up to record data at least every fifteen minutes. We attempted to collect a
minimum of three weeks of data before installation of the new EMS, and another three
weeks of data after installation of the EMS. Table 3 below lists the installation dates and
monitoring periods in this study.
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PG&E’s Emerging Technologies Program ET12PGE3461
TABLE 3. INSTALLATION DATES & MONITORING PERIODS FOR ELEVEN PROJECTS
Note that monitoring periods were less than three weeks for two facilities, although we were
still able to analyze data from these buildings:
The post-installation monitoring period was 11 days at the Alameda County Fast
Food Restaurant. We were told that installation of the EMS took place on December
13, 2013. However, the online EMS data for this site is not available until after
January 8, 2014.
The lighting circuits at the Lake County Assembly facility were initially connected
incorrectly to the EMS, so the wrong systems were being controlled. The problems
were fixed by December 28, 2013, so the post-installation period for lighting analysis
was 19 days.
In addition, lighting at the Nevada County Assembly facility was not connected to the EMS,
since the lighting system was already easily controlled by the facility staff. We kept the
results from the Nevada County facility in our final results, since the use of the EMS to
control only HVAC use is likely to occur in various facilities.
One major challenge for this study, regarding determining HVAC savings, was the
extrapolation to annual savings estimates from data covering only an incomplete range of
weather conditions. Most of the installations were completed during the winter season, so
that many sites experienced few or no hours of warm weather during the logging periods.
Table 3 above also lists how many HVAC units or lighting circuits were successfully
monitored at each facility. We tried to monitor the energy use of all operating HVAC units at
Location Building TypeMonitoring
Start
EMS
Installation
Monitoring
Finish
Pre Period
Days
Post Period
Days
HVAC Units
Monitored
Lighting
Circuits
Monitored
Kern County Retail 10/29/2013 12/20/2013 1/14/2014 51 24 3 3
Alameda
CountyRetail 11/19/2013 12/14/2013 1/20/2014 24 36 1 5
Nevada
CountyAssembly 10/29/2013 12/11/2013 1/16/2014 42 35 3 5
Lake County Assembly 10/30/2013 12/12/2013 1/16/2014 42 34 3 5
Stanislaus
County
Assembly /
Restaurant10/29/2013 12/17/2013 1/20/2014 48 33 8 11
Santa Clara
County
Assembly /
Restaurant11/19/2013 12/16/2013 1/26/2014 26 40 9 11
Santa Clara
County
Sit Down
Restaurant6/17/2013 7/31/2013 8/30/2013 43 29 5 5
San Joaquin
County
Sit Down
Restaurant6/17/2013 7/22/2013 8/8/2013 34 16 3 3
Alameda
County
Fast Food
Restaurant11/12/2013
12/13/2013-
1/8/20141/20/2014 31 11 5 6
Placer CountyFast Food
Restaurant9/10/2013 10/24/2013 11/19/2013 43 25 3 3
Merced
County
Fast Food
Restaurant12/12/2013 1/2/2014 1/26/2014 20 23 5 6
12
PG&E’s Emerging Technologies Program ET12PGE3461
each facility, although we had a couple of data collection problems at one facility. At the
Lake County Assembly facility, three of the six loggers on AC units failed because of two
batteries that died unexpectedly, and one current transducer that opened sometime after
installation. At the Placer County fast food restaurant, one of the four rooftop units was
switched off (disconnect open) when we installed our loggers, and it was later turned on,
contributing cooling which the other units then did not need to provide. HVAC data for this
site was excluded from the results.
We did not monitor all lighting circuits at each facility, in order to keep the time and
expenses of this study at a reasonable level. We instead monitored a statistically significant
sample of lighting in each of the different space types at each facility. For example, in a sit
down restaurant we monitored lighting in the kitchen and at two or more areas of the dining
room. Larger facilities with more varied types of spaces had more lighting circuits
monitored. All of our installed lighting loggers appeared to function correctly over the
monitoring period.
EMS ONLINE DATA The EMS installed in these facilities has an online site where settings can be examined, and
energy use and temperature trends can be reviewed. We accessed the online sites for the
eleven facilities to collect the following information for the post-installation period:
Lighting control schedules
Thermostat settings and schedules for HVAC equipment
Trends in the total energy use of the facility
There is only a limited amount of historic total energy use data available for each facility.
The EMS saves energy use from the day of installation, but only the total facility energy use
for each day was made available and evaluated for this study.
13
PG&E’s Emerging Technologies Program ET12PGE3461
ENERGY SAVINGS ANALYSIS AND RESULTS The analysis of eleven facilities included:
Analysis of monitored lighting energy use before and after the EMS installation
Analysis of monitored HVAC energy use before and after the EMS installation, and
extrapolation to estimated annual savings based on typical weather data.
Table 4 below includes a list of the weather data and building energy use reference data we
used to annualize our monitored results to typical weather conditions, and to normalize the
results to the building type and location.
14
PG&E’s Emerging Technologies Program ET12PGE3461
TABLE 4. LOCAL WEATHER DATA, ANNUAL WEATHER DATA, AND BUILDING ENERGY USE REFERENCE DATA USED TO
ANNUALIZE AND NORMALIZE MONITORING RESULTS
LOCAL WEATHER DATA DURING
MONITORING PERIOD ANNUAL
WEATHER
FILE USED
(2010
CEC
FILES)
BUILDING ENERGY USE REFERENCE DATA BY BUILDING
TYPE & LOCATION
Location, Type
Airport Airport
Call Letters
CEUS Building Type
CEUS Climate Zone
# of CEUS Buildings
Kern County Retail
Bakersfield BFL CEC CZ13 Retail, all types Average of
Central Valley & Desert
8 & 1
Alameda
County Retail
Livermore LVK Livermore
AP Retail, all types
Average of
Central Coast & Central Valley
64
& 128
Nevada
County Assembly
Grass Valley
GOO CEC CZ11 Assembly, all types Central Valley 77
Lake County Assembly
Ukiah UKI CEC CZ2 Assembly, all types North Coast 13
Stanislaus
County Assembly/ Restaurant
Modesto MOD CEC CZ12
Average of
Assembly, all types & Restaurant, all
types
Central Valley 77 & 43
Santa Clara County
Assembly/ Restaurant
San Jose SJC CEC CZ4
Average of Assembly, all types
& Restaurant, all types
Central Coast 54 & 31
South Santa Clara
County Sit Down
Restaurant
Hollister CVH CEC CZ4 Restaurant, all
types Central Coast 31
San Joaquin County
Sit Down Restaurant
Stockton SCK CEC CZ12 Restaurant, all
types Central Valley 43
Alameda County
Fast Food Restaurant
Livermore LVK CEC CZ12 Restaurant, all
types
Average of Central Coast &
Central Valley
31 &
43
Placer
County Fast Food
Restaurant
Lincoln LHM CEC CZ11 Restaurant, all
types
Average of Central Valley
& Mountain
43 &
2
Merced County
Fast Food Restaurant
Merced MCE CEC CZ12 Restaurant, all
types Central Valley 43
15
PG&E’s Emerging Technologies Program ET12PGE3461
LIGHTING ANALYSIS We analyzed the current drawn by monitored lighting equipment before and after the
installation of the EMS at each of the eleven facilities. This data is presented in plots of
average daily current draw that were developed from our collected data via the following
steps:
Monitored current from all lighting circuits was added together to get a total lighting
current draw at each time step over the monitoring period
Each time step was assigned to either the pre-installation or post-installation period
The day when EMS installation took place was not included in the analysis. To make
sure we had the correct installation day, we cross-checked to make sure the
installation date we were given from OEM personnel was the same as the first day of
collected data that was stored in the online control system for each facility. (This was
only a problem in one facility. System data availability suggests that the EMS
installation at the Alameda County Fast Food Restaurant may not have occurred until
January 8, 2014, although we were told that it took place on December 13, 2013.)
Operating hours in each facility were separated into times when the facility was
“occupied” or “unoccupied”. A third category was designated in the two Sit Down
Restaurants as “staff”, or the time when only the kitchen and wait staff were in the
facility preparing for the day or cleaning up afterwards, but no customers were
present. These different operating periods were designated according to what we
found in each facility’s EMS online control system settings.
Current was averaged at each time of the day, to get average daily lighting curves
for the pre- and post-installation periods
The average daily lighting currents were summed into the following categories:
o Pre-installation Unoccupied
o Pre-installation Staff (if the facility includes this category)
o Pre-installation Occupied
o Post-installation Unoccupied
o Post-installation Staff (if the facility includes this category)
o Post-installation Occupied
Percent savings were calculated between the pre- and post-installation current sums
for the Unoccupied, Staff and Occupied categories, and then summed into an overall
percent savings in lighting use for all time categories
Annualizing Percent Savings
In order to scale the lighting monitoring results up to the entire year, we assumed
that the savings found during the monitoring period would apply over a whole year.
HVAC ANALYSIS The analysis of HVAC energy use was similar to our analysis of lighting use. However, since
HVAC use is highly dependent on outside temperature, we used our collected data to find
correlations to annualize the energy use and savings results.
16
PG&E’s Emerging Technologies Program ET12PGE3461
We analyzed the current drawn by monitored HVAC equipment before and after the
installation of EMS at each of the eleven facilities. This data is presented in plots of average
daily current draw (see Appendix D) that were developed from our collected data via the
following steps:
Monitored current from all HVAC circuits was added together to get a total HVAC
current draw at each time step over the monitoring period.
Each time step was assigned to either the pre-installation or post-installation period.
The day when EMS installation took place was not included in the analysis. To make
sure we had the correct installation day, we cross-checked the installation date we
were given from OEM personnel with the first day of collected data stored in the
online control system for each facility. (This was only an issue in one facility, the
Alameda County Fast Food Restaurant, where system data was not available until
January 8, 2014, nearly a month after system installation was reported to have
taken place on December 13, 2013.).
Operating hours in each facility were separated into times when the facility was
“occupied” or “unoccupied”. A third category was designated in the two Sit Down
Restaurants as “staff”, or the time when only the kitchen and wait staff were in the
facility before and after customer hours and that use different heating and cooling
set points. These operating periods were designated according to what we found in
each facility’s EMS online control system settings.
Current was averaged at each time of the day, to get average daily HVAC curves for
the pre- and post-installation periods. The average daily HVAC currents were
summed into the following categories:
o Pre-installation Unoccupied Hours
o Pre-installation Staff Hours (if the facility includes this category)
o Pre-installation Occupied Hours
o Post-installation Unoccupied Hours
o Post-installation Staff Hours (if the facility includes this category)
o Post-installation Occupied Hours
Annualizing HVAC Percent Savings
To scale the HVAC monitoring results to a typical year, we took the following steps:
o Outdoor temperatures were determined for each hour in the monitoring
periods at each site in the above categories. We did not measure outdoor
temperatures on site, but used weather data at the airport weather station
closest to each facility. The airport weather data used for each location is
listed in Table 4. Note that the current EMS configuration does not include
monitoring of outdoor air temperatures, and nearby airport weather data
provides high reliability.
o HVAC current monitored values were averaged into outdoor temperature bins
of 2-degrees Fahrenheit. Correlations were then established between HVAC
current and 2-degree outdoor temperature bins for each category of time
(Pre-Installation Unoccupied Hours, Pre-Installation Staff Hours, Pre-
17
PG&E’s Emerging Technologies Program ET12PGE3461
Installation Occupied Hours, Post-Installation Unoccupied Hours, Post-
Installation Staff Hours, and Post-Installation Occupied Hours).
Most of the monitoring was completed in winter conditions. For most sites,
little or no data was collected in higher outside temperature conditions, when
economizers are closed and cooling is by (energy-intensive) compressor only.
Since these warm conditions mostly occur in the afternoons, when the sites
remained occupied and fully conditioned, the installation of the EMS normally
impact energy use during such periods only if the EMS is used to adjust
occupied cooling setpoints.
The correlations were as follows (See example graphs below):
For each 2-degree outdoor temperature bin value within the range of
monitored outdoor temperatures, the average HVAC current value
monitored at that outdoor temperature was calculated.
For each 2-degree outdoor temperature bin value below the range of
monitored outdoor temperatures, a linear extrapolation was done to
obtain the corresponding HVAC current value. The expression for the
linear extrapolation was Current = m x Tout + B, where m is the slope
of the line and B is the y-intercept. Note that heating in all cases was
gas-fired, so electricity use for HVAC in cold conditions is for fan
cycling only.
For each 2-degree outdoor temperature bin value above the range of
monitored outdoor temperatures, an extrapolation was performed
similar to that described above, generally using data for outside air
temperatures above 65 °F (cooling mode), for the base case. Due to
limited data in some cases, some engineering judgment was
necessary. No savings were calculated for these conditions. The annual
kWh usage projected for these conditions was included in the
denominator of the % savings calculations.
o Outdoor temperatures for an entire typical year were correlated into bins of
2-degree Fahrenheit increments. Annual temperatures were pulled from TMY3
data files developed by the California Energy Commission for sixteen
California climate zones. Table 4 lists the CEC climates zones that were used
for each facility in this study. Note that the Alameda County Retail facility
used TMY3 data from the Livermore airport, instead of the CEC climate zone
data.
o HVAC current was calculated from the derived correlations for each 2-degree
bin temperature value, multiplied by the number of hours in each bin, and
then summed to get the entire year’s estimated energy use under the
operating parameters for each category. Annual savings was found by
subtracting post-installation energy use from pre-installation energy use.
An example of HVAC current and outdoor temperature correlation results is shown for the
Santa Clara County sit-down restaurant site in Figures 1A-C, below. The dots indicate the
average HVAC current values per temperature bin as calculated from monitoring data. The
solid lines represent the model correlating the data. As seen in the graph for occupied hours
(Figure 1A, below), the range of monitored outdoor temperatures for which both Pre and
Post-installation data was obtained, is 54 to 80 °F. The model follows the monitoring data
points within this outdoor temperature range. Below 54 °F, the model follows a linear
18
PG&E’s Emerging Technologies Program ET12PGE3461
extrapolation of the monitoring values. Above 80 °F, the model does not extrapolate values
and instead sets the HVAC Current value to zero for both pre and post-installation cases.
Finally, the solid olive line indicates annual hours per temperature bin in the TMY3 weather
data. The model only correlates HVAC current to outdoor temperature in the range of hours
in a typical year. For instance, in the example figure below, HVAC current in the model is
set to zero below 38 °F since there are no instances of outside air temperatures below 38 °F
in this typical year dataset.
FIGURE 1A. EXAMPLE HVAC ENERGY VS OUTSIDE AIR TEMPERATURE – OCCUPIED HOURS
0
200
400
600
0
50
100
150
200
250
300
20 40 60 80 100 120
TMY3
Ho
urs
Avg
. HV
AC
Cu
rren
t (A
)
Outdoor Temperature (°F)
HVAC VS OAT_Occ - Santa Clara County(Sit Down Restaurant)
Occ Occ
Pre Occ_Model Post Occ_Model
Weather TMY3 Hours
19
PG&E’s Emerging Technologies Program ET12PGE3461
FIGURE 1B. EXAMPLE HVAC ENERGY VS OUTSIDE AIR TEMPERATURE – STAFF HOURS
FIGURE 1C. EXAMPLE HVAC ENERGY VS OUTSIDE AIR TEMPERATURE – UNOCCUPIED HOURS
0
200
400
600
0
50
100
150
200
20 40 60 80 100
TMY3
Ho
urs
Avg
. HV
AC
Cu
rren
t (A
)
Outdoor Temperature (°F)
HVAC VS OAT_Staff - Santa Clara County(Sit Down Restaurant)
Staff StaffPre Staff_Model Post Staff_ModelWeather TMY3 Hours
0
200
400
600
0
20
40
60
20 40 60 80 100
TMY3
Ho
urs
Avg
. HV
AC
Cu
rren
t (A
)
Outdoor Temperature (°F)
HVAC VS OAT_Unocc - Santa Clara County
(Sit Down Restaurant)
Unocc Unocc
Pre Unocc_Model Post Unocc_Model
Weather TMY3 Hours
20
PG&E’s Emerging Technologies Program ET12PGE3461
The HVAC energy data at three sites was problematic. Following is a brief discussion of each
issue and the remedy applied. See also graphs of HVAC energy vs. outside air temperature
(OAT) in Appendix C.
1. Data for the Placer County fast food restaurant shows a very large drop in HVAC
energy use after EMS installation, which indicates changes beyond what the EMS
would impact. See Figure 2 below. We excluded HVAC savings in reported results for
this site. One possible contributor to the drop includes the fact that one of the four
rooftop units was switched off (disconnect open) when we installed our loggers, and
it was later turned on, contributing cooling which the other units then did not need to
provide.
FIGURE 2. DAILY AVERAGE MONITORED HVAC USE AT PLACER COUNTY FAST FOOD RESTAURANT FACILITY, PRE &
POST EMS
2. Data for the Stanislaus County assembly/restaurant facility (a golf and country club)
shows very little HVAC current use during the post-installation period, in late
December and January. See Figure 3 below. We excluded HVAC savings in reported
results for this site as well. Weather was little changed. We suspect that the data
primarily indicates seasonality of occupancy, and low current use during infrequent
(gas) heating. That is, it appears that the club was largely unoccupied during the
January post-installation monitoring period.
0
10
20
30
40
50
60
70
80
0
10
20
30
40
50
60
70
80
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Ou
tdo
or
Tem
pera
ture
, d
eg
F
Avg
HV
AC
To
tal
Before 10-24-2013 - Occ - Average of HVAC Total Before 10-24-2013 - Unocc - Average of HVAC Total
After 10-24-2013 - Occ - Average of HVAC Total After 10-24-2013 - Unocc - Average of HVAC Total
Before 10-24-2013 - Occ - Average of TOUT Before 10-24-2013 - Unocc - Average of TOUT
After 10-24-2013 - Occ - Average of TOUT After 10-24-2013 - Unocc - Average of TOUT
21
PG&E’s Emerging Technologies Program ET12PGE3461
FIGURE 3. DAILY AVERAGE MONITORED HVAC USE AT STANISLAUS COUNTY ASSEMBLY/RESTAURANT FACILITY, PRE & POST EMS
3. Finally, unusual data was observed at the Kern County retail clinic site for the
occupied post-installation periods. Measured HVAC current at this site drops
effectively to zero every day at 11:00 am and remains close to zero for the rest of
the Occupied hours. See Figure 4 below. We excluded HVAC savings during occupied
hours in reported results for this site. We suspect that the occupied hours were set
incorrectly in the EMS, where the end of the period was entered as 11:00 am instead
of 11:00 pm. Oddly, the site did not report any problems in the customer survey,
even though their HVAC is shutting off at 11:00 am each day.
FIGURE 4. DAILY AVERAGE MONITORED HVAC USE AT KERN COUNTY RETAIL CLINIC FACILITY, PRE & POST EMS
HOURS
0
10
20
30
40
50
60
70
0
5
10
15
20
25
30
35
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Ou
tdo
or
Tem
pera
ture
, d
eg
F
Avg
HV
AC
To
tal
Before 12-17-13 - Occ - Average of HVAC Total Before 12-17-13 - Unocc - Average of HVAC Total
After 12-17-2013 - Occ - Average of HVAC Total After 12-17-2013 - Unocc - Average of HVAC Total
Before 12-17-13 - Occ - Average of TOUT Before 12-17-13 - Unocc - Average of TOUT
After 12-17-2013 - Occ - Average of TOUT After 12-17-2013 - Unocc - Average of TOUT
22
PG&E’s Emerging Technologies Program ET12PGE3461
TABLE 6. CEUS ELECTRICITY USE PER FLOOR AREA USED TO NORMALIZE SAVINGS IN THE FACILITIES
Location Building Type
Lighting
kWh/sf
HVAC
kWh/sf
Other
kWh/sf
Total
kWh/sf
Kern County Retail 104 64 38 206
Alameda County Retail 140 48 66 253
Nevada County Assembly 34 26 40 100
Lake County Assembly 70 27 81 178
Stanislaus County Assembly / Restaurant 94 114 242 449
Santa Clara County Assembly / Restaurant 94 46 259 398
Santa Clara County Sit Down Restaurant 154 83 477 714
San Joaquin County Sit Down Restaurant 153 201 443 798
Alameda County Fast Food Restaurant 153 142 460 756
Placer County Fast Food Restaurant 155 165 363 682
Merced County Fast Food Restaurant 153 201 443 798
AVERAGE 118 101 265 485
23
PG&E’s Emerging Technologies Program ET12PGE3461
PRE- AND POST-EMS INSTALLATION RESULTS The following section provides the analysis results for the sites where the EMS was installed.
Table 7 shows a summary of the annual expected lighting and HVAC savings from the
installation of the EMS, as well as the overall building energy use savings. The table gives %
savings as well as a normalized value of energy use per floor area (kWh/sf).
The EMS system produced the most savings from its control of HVAC use, with included
HVAC savings ranging from 6% to 31%. On average, facilities were projected to save 18%
of their annual HVAC electricity use.
The EMS was less likely to save electricity when controlling lighting. Five of eleven facilities
used slightly more lighting energy after the EMS was installed, although the Nevada County
facility did not actually hook their lighting up to the EMS at all. The other six facilities had
lighting energy savings ranging from about 1% to 36%. Overall, facilities saved an average
of 6% of their lighting energy use with the EMS.
24
PG&E’s Emerging Technologies Program ET12PGE3461
TABLE 7. ANNUAL LIGHTING AND HVAC ELECTRICITY SAVINGS DUE TO EMS INSTALLATION, IN PERCENT
Discussion of these results and variations is provided in the following sections, separately
for lighting and HVAC energy impacts.
LIGHTING MONITORING SAVINGS RESULTS Below we present details of the lighting savings found in the eleven facilities. Note that only
a sample of lighting circuits was monitored in each facility, so the overall level of lighting
current does not reflect the entire lighting energy use in the building. However, the lighting
use trends are expected to be representative of the control provided to the building’s
lighting system before and after the EMS was installed.
LocationCalifornia
Climate ZoneBuilding Type Lighting %
Savings
HVAC %
Savings
Kern County 13 Retail 14% 6%
Alameda
County12 Retail 0% 20%
Nevada
County11 Assembly - 25%
Lake County 2 Assembly 36% 9%
Stanislaus
County12
Assembly /
Restaurant7% -
Santa Clara
County4
Assembly /
Restaurant-4% 31%
Santa Clara
County4
Sit Down
Restaurant-6% 19%
San Joaquin
County12
Sit Down
Restaurant8% 28%
Alameda
County12
Fast Food
Restaurant-5% 18%
Placer County 11Fast Food
Restaurant17% -
Merced
County12
Fast Food
Restaurant1% 11%
AVERAGE 6% 18%
25
PG&E’s Emerging Technologies Program ET12PGE3461
Table 8 summarizes the lighting energy savings during occupied, staff, and unoccupied
periods in each facility.
TABLE 8. LIGHTING ELECTRICITY SAVINGS FROM EMS INSTALLATION DURING OCCUPIED, STAFF & UNOCCUPIED PERIODS
Savings in lighting energy use was most likely to occur during staff or unoccupied hours,
with 9 of 11 facilities saving between 3% and 51% of their lighting energy while the building
was unoccupied. Both facilities with staff-only periods saved lighting energy during those
hours, 3% and 11%. In contrast, only 5 of 11 facilities saved lighting energy during
occupied hours, with savings ranging from 2% to 41%. Overall, 6 of 11 facilities saved
lighting energy, for an average savings of 6%.
For many of these small commercial sites, lighting was already fairly well-controlled, with
staff shutting off lights at the end of the day. Although average savings from all lighting
control was just 6%, large lighting energy savings were measured in some facilities. The
Placer County fast food restaurant achieved 51% savings during unoccupied hours, and
41%/32% occupied/unoccupied savings were measured at the Lake County assembly
facility.
In this sample, there was no discernible pattern of lighting savings by building type. For
example, lighting savings for two very similar sit-down restaurants were -6% and 8%. Two
Location Building Type Occupied Staff Unoccupied Total
Kern County Retail 9% 29% 14%
Alameda County Retail -2% 3% 0%
Nevada County Assembly - - -
Lake County Assembly 41% 32% 36%
Stanislaus County Assembly / Restaurant 2% 22% 7%
Santa Clara County Assembly / Restaurant -4% -5% -4%
Santa Clara County Sit Down Restaurant -6% 3% -29% -6%
San Joaquin County Sit Down Restaurant 5% 11% 15% 8%
Alameda County Fast Food Restaurant -8% 3% -5%
Placer County Fast Food Restaurant 14% 51% 17%
Merced County Fast Food Restaurant -2% 18% 1%
AVERAGE 5% 7% 14% 7%
26
PG&E’s Emerging Technologies Program ET12PGE3461
very similar fast food restaurants showed lighting savings of -5% and 1%. Two similar
assembly/restaurant facilities achieved -4% and 7% lighting energy savings.
Lighting energy savings depended on how well staff controlled lighting before the EMS
installation, and whether the EMS was used to more aggressively have lights turn off
automatically using the EMS.
27
PG&E’s Emerging Technologies Program ET12PGE3461
Figures 5A through 5J, on the following pages, plot the lighting current monitored in each
facility before and after the EMS installation. Each plot shows the average daily current
drawn both before and after the EMS was installed. In addition, the different periods of use
(occupied, staff, and unoccupied) are delineated by different colored lines. The horizontal
axis of each plot shows the time of day, and the vertical axis is in amps of current. Note
that vertical scales of lighting current are different in each plot.
28
PG&E’s Emerging Technologies Program ET12PGE3461 FIGURE 5A. DAILY AVERAGE MONITORED LIGHTING USE AT KERN COUNTY RETAIL
CLINIC FACILITY, PRE & POST EMS
FIGURE 5B. DAILY AVERAGE MONITORED LIGHTING USE AT ALAMEDA COUNTY RETAIL
FACILITY, PRE & POST EMS
FIGURE 5C. DAILY AVERAGE MONITORED LIGHTING USE AT NEVADA COUNTY
ASSEMBLY FACILITY, PRE & POST EMS
FIGURE 5D. DAILY AVERAGE MONITORED LIGHTING USE AT LAKE COUNTY ASSEMBLY
FACILITY, PRE & POST EMS
0
2
4
6
8
10
12
14
16
18
12:0
3 A
M
2:0
3 A
M
4:0
3 A
M
6:0
3 A
M
8:0
3 A
M
10:0
3 A
M
12:0
3 P
M
2:0
3 P
M
4:0
3 P
M
6:0
3 P
M
8:0
3 P
M
10:0
3 P
M
Avg
Lig
hti
ng
To
tal
Pre Occ Pre Unocc Post Occ Post Unocc
0
5
10
15
20
25
30
35
40
45
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Avg
Lig
hti
ng
To
tal
Pre Occ Pre Unocc Post Occ Post Unocc
0
1
2
3
4
5
6
7
8
9
10
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Avg
Lig
hti
ng
To
tal
Pre Occ Pre Unocc Post Occ Post Unocc
0
10
20
30
40
50
60
70
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Avg
Lig
hti
ng
To
tal
Pre Occ Pre Unocc Post Occ Post Unocc
29
PG&E’s Emerging Technologies Program ET12PGE3461 FIGURE 5E. DAILY AVERAGE MONITORED LIGHTING USE AT STANISLAUS COUNTY
ASSEMBLY/RESTAURANT FACILITY, PRE & POST EMS
FIGURE 5F. AVERAGE MONITORED DAILY LIGHTING USE AT SANTA CLARA COUNTY
ASSEMBLY/RESTAURANT FACILITY, PRE & POST EMS
FIGURE 5G. DAILY AVERAGE MONITORED LIGHTING USE AT SOUTH SANTA CLARA
COUNTY SIT DOWN RESTAURANT FACILITY, PRE & POST EMS
FIGURE 5H. DAILY AVERAGE MONITORED LIGHTING USE AT SAN JOAQUIN COUNTY SIT
DOWN RESTAURANT FACILITY, PRE & POST EMS
0
10
20
30
40
50
60
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Avg
Lig
hti
ng
To
tal
Pre Occ Pre Unocc Post Occ Post Unocc
0
20
40
60
80
100
120
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Avg
Lig
hti
ng
To
tal
Pre Occ Pre Unocc Post Occ Post Unocc
0
1
2
3
4
5
6
7
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Avg
Lig
hti
ng
To
tal
Pre Occ Pre Staff Pre Unocc Post Occ Post Staff Post Unocc
0
1
2
3
4
5
6
7
8
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Avg
Lig
hti
ng
To
tal
Pre Occ Pre Staff Pre Unocc Post Occ Post Staff Post Unocc
30
PG&E’s Emerging Technologies Program ET12PGE3461 FIGURE 5I. DAILY AVERAGE MONITORED LIGHTING USE AT ALAMEDA COUNTY FAST
FOOD RESTAURANT FACILITY, PRE & POST EMS
FIGURE 5J. DAILY AVERAGE MONITORED LIGHTING USE AT PLACER COUNTY FAST
FOOD RESTAURANT FACILITY, PRE & POST EMS
FIGURE 5K. DAILY AVERAGE MONITORED LIGHTING USE AT MERCED COUNTY FAST
FOOD RESTAURANT FACILITY, PRE & POST EMS
0
5
10
15
20
25
30
35
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Avg
Lig
hti
ng
To
tal
Pre Occ Pre Unocc Post Occ Post Unocc
0
5
10
15
20
25
30
35
40
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Avg
Lig
hti
ng
To
tal
Pre Occ Pre Unocc Post Occ Post Unocc
31
PG&E’s Emerging Technologies Program ET12PGE3461
HVAC MONITORING SAVINGS RESULTS Below we present details of the HVAC savings found in the eleven facilities due to the EMS
installation. Note that we monitored a sample of the operating HVAC units in these facilities,
so the overall level of current does not reflect the entire HVAC energy use in the building.
However, the HVAC trends are expected to be a representative sample of the control
provided to the building before and after the EMS was installed, from which an overall
savings percentage is determined.
Table 9 summarizes the annualized HVAC energy savings during occupied, staff, and
unoccupied periods in each facility. Values highlighted red in this table indicate data that
was excluded from the analysis based on the anomalous information gathered, as described
above.
In all facilities, savings in HVAC energy use is higher during staff or unoccupied hours than
it is during occupied hours. Savings during unoccupied hours ranged from 13% to 73%,
with an average of 33% savings. Savings in the two facilities with staff hours saved even
more energy during this time, with an average of 60% savings.
During occupied hours, seven facilities had confirmed positive savings between 4 and 30%
of their HVAC energy use, while one facility used more energy during these hours.
In terms of overall HVAC savings, the nine facilities with confirmed HVAC savings used an
average of 18% less HVAC energy after the EMS was installed.
The ability to make broad conclusions from this study were impacted by the small number
of sites monitored (a common challenge with technology assessments), the limited range of
weather conditions monitored (with installation occurring in winter at many sites), and some
site anomalies and logger issues.
32
PG&E’s Emerging Technologies Program ET12PGE3461
TABLE 9. HVAC ELECTRICITY SAVINGS FROM EMS INSTALLATION DURING OCCUPIED, STAFF & UNOCCUPIED PERIODS
Figures 6A through 6J, on the following pages, plot the HVAC current monitored in each
facility before and after the EMS installation. Each plot shows the average daily current
drawn both before and after the EMS was installed. In addition, the different periods of use
(occupied, staff, and unoccupied) are delineated by different colored lines. The horizontal
axis of each plot shows the time of day, and the vertical axis is in amps of current. Note
that vertical scales of HVAC current are different in each plot.
The average outdoor air temperatures are also included on Figures 6A through 6J. In all but
three facilities, our monitoring took place during fall or winter conditions, with average
outdoor temperatures at just 60°F or lower. The other three facilities (South Santa Clara
County, San Joaquin County, and Placer County) were monitored under warmer, summer
conditions.
Location Building Type Occupied Staff Unoccupied Total
Kern County Retail - 17% 6%
Alameda County Retail 5% 37% 20%
Nevada County Assembly -5% 55% 25%
Lake County Assembly 4% 17% 9%
Stanislaus County Assembly / Restaurant - - -
Santa Clara County Assembly / Restaurant 30% 32% 31%
Santa Clara County Sit Down Restaurant 11% 57% 33% 19%
San Joaquin County Sit Down Restaurant 17% 62% 73% 28%
Alameda County Fast Food Restaurant 17% 21% 18%
Placer County Fast Food Restaurant - - -
Merced County Fast Food Restaurant 9% 13% 11%
AVERAGE 11% 60% 33% 18%
33
PG&E’s Emerging Technologies Program ET12PGE3461
TABLE 10. EXTREME OUTDOOR LOW AND HIGH TEMPERATURES DURING EACH FACILITY’S MONITORING PERIOD
Location Building TypeMonitoring
Start
EMS
Installation
Monitoring
Finish
Outdoor
Low
Outdoor
High
Kern County Retail 10/29/2013 12/20/2013 1/14/2014 28°F 84°F
Alameda
CountyRetail 11/19/2013 12/14/2013 1/20/2014 26°F 73°F
Nevada
CountyAssembly 10/29/2013 12/11/2013 1/16/2014 21°F 72°F
Lake County Assembly 10/30/2013 12/12/2013 1/16/2014 20°F 82°F
Stanislaus
County
Assembly /
Restaurant10/29/2013 12/17/2013 1/20/2014 25°F 75°F
Santa Clara
County
Assembly /
Restaurant11/19/2013 12/16/2013 1/26/2014 28°F 72°F
Santa Clara
County
Sit Down
Restaurant6/17/2013 7/31/2013 8/30/2013 46°F 100°F
San Joaquin
County
Sit Down
Restaurant6/17/2013 7/22/2013 8/8/2013 53°F 107°F
Alameda
County
Fast Food
Restaurant11/12/2013
12/13/2013-
1/8/20141/20/2014 27°F 79°F
Placer CountyFast Food
Restaurant9/10/2013 10/24/2013 11/19/2013 32°F 91°F
Merced
County
Fast Food
Restaurant12/12/2013 1/2/2014 1/26/2014 25°F 72°F
34
PG&E’s Emerging Technologies Program ET12PGE3461 FIGURE 6A. DAILY AVERAGE MONITORED HVAC USE AT KERN COUNTY RETAIL CLINIC
FACILITY, PRE & POST EMS
FIGURE 6B. DAILY AVERAGE MONITORED HVAC USE AT ALAMEDA COUNTY RETAIL
FACILITY, PRE & POST EMS
FIGURE 6C. DAILY AVERAGE MONITORED HVAC USE AT NEVADA COUNTY ASSEMBLY
RESTAURANT FACILITY, PRE & POST EMS
FIGURE 6D. DAILY AVERAGE MONITORED HVAC USE AT LAKE COUNTY ASSEMBLY
FACILITY, PRE & POST EMS
0
10
20
30
40
50
60
70
0
2
4
6
8
10
12
14
12:0
3 A
M
2:0
3 A
M
4:0
3 A
M
6:0
3 A
M
8:0
3 A
M
10:0
3 A
M
12:0
3 P
M
2:0
3 P
M
4:0
3 P
M
6:0
3 P
M
8:0
3 P
M
10:0
3 P
M
Ou
tdo
or
Tem
pera
ture
, d
eg
F
Avg
HV
AC
To
tal
Before 12-20-13 - Occ - Average of HVAC Total Before 12-20-13 - Unocc - Average of HVAC Total
After 12-20-13 - Occ - Average of HVAC Total After 12-20-13 - Unocc - Average of HVAC Total
Before 12-20-13 - Occ - Average of TOUT Before 12-20-13 - Unocc - Average of TOUT
After 12-20-13 - Occ - Average of TOUT After 12-20-13 - Unocc - Average of TOUT
0
10
20
30
40
50
60
70
0
2
4
6
8
10
12
14
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Ou
tdo
or
Tem
pera
ture
, d
eg
F
Avg
HV
AC
To
tal
Before 12-12-13 - Occ - Average of HVAC Total Before 12-12-13 - Unocc - Average of HVAC Total
After 12-12-13 - Occ - Average of HVAC Total After 12-12-13 - Unocc - Average of HVAC Total
Before 12-12-13 - Occ - Average of TOUT Before 12-12-13 - Unocc - Average of TOUT
After 12-12-13 - Occ - Average of TOUT After 12-12-13 - Unocc - Average of TOUT
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Ou
tdo
or
Tem
pera
ture
, d
eg
F
Avg
HV
AC
To
tal
Before 12-11-13 - Occ - Average of HVAC Total Before 12-11-13 - Unocc - Average of HVAC Total
After 12-11-13 - Occ - Average of HVAC Total After 12-11-13 - Unocc - Average of HVAC Total
Before 12-11-13 - Occ - Average of TOUT Before 12-11-13 - Unocc - Average of TOUT
After 12-11-13 - Occ - Average of TOUT After 12-11-13 - Unocc - Average of TOUT
0
10
20
30
40
50
60
70
0
2
4
6
8
10
12
14
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Ou
tdo
or
Tem
pera
ture
, d
eg
F
Avg
HV
AC
To
tal
Before 12-12-13 - Occ - Average of HVAC Total Before 12-12-13 - Unocc - Average of HVAC Total
After 12-12-13 - Occ - Average of HVAC Total After 12-12-13 - Unocc - Average of HVAC Total
Before 12-12-13 - Occ - Average of TOUT Before 12-12-13 - Unocc - Average of TOUT
After 12-12-13 - Occ - Average of TOUT After 12-12-13 - Unocc - Average of TOUT
35
PG&E’s Emerging Technologies Program ET12PGE3461 FIGURE 6E. DAILY AVERAGE MONITORED HVAC USE AT STANISLAUS COUNTY
ASSEMBLY/RESTAURANT FACILITY, PRE & POST EMS
FIGURE 6F. DAILY AVERAGE MONITORED HVAC USE AT SANTA CLARA COUNTY
ASSEMBLY/RESTAURANT FACILITY, PRE & POST EMS
FIGURE 6G. DAILY AVERAGE MONITORED HVAC USE AT SOUTH SANTA CLARA
COUNTY SIT DOWN RESTAURANT FACILITY, PRE & POST EMS
FIGURE 6H. DAILY AVERAGE MONITORED HVAC USE AT SAN JOAQUIN COUNTY SIT
DOWN RESTAURANT FACILITY, PRE & POST EMS
0
10
20
30
40
50
60
70
0
5
10
15
20
25
30
35
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Ou
tdo
or
Tem
pera
ture
, d
eg
F
Avg
HV
AC
To
tal
Before 12-17-13 - Occ - Average of HVAC Total Before 12-17-13 - Unocc - Average of HVAC Total
After 12-17-2013 - Occ - Average of HVAC Total After 12-17-2013 - Unocc - Average of HVAC Total
Before 12-17-13 - Occ - Average of TOUT Before 12-17-13 - Unocc - Average of TOUT
After 12-17-2013 - Occ - Average of TOUT After 12-17-2013 - Unocc - Average of TOUT
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Ou
tdo
or
Tem
pera
ture
, d
eg
F
Avg
HV
AC
To
tal
Before 12-16-13 - Occ - Average of HVAC Total Before 12-16-13 - Unocc - Average of HVAC Total
After 12-16-13 - Occ - Average of HVAC Total After 12-16-13 - Unocc - Average of HVAC Total
Before 12-16-13 - Occ - Average of TOUT Before 12-16-13 - Unocc - Average of TOUT
After 12-16-13 - Occ - Average of TOUT After 12-16-13 - Unocc - Average of TOUT
0
10
20
30
40
50
60
70
80
0
30
60
90
120
150
180
210
240
12
:00
AM
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10
:00
AM
12
:00
PM
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10
:00
PM
Ou
tdo
or
Tem
pe
ratu
re, d
eg
F
Avg
HV
AC
To
tal
Before 7-30-13 - Occ - Avg HVAC Total Before 7-30-13 - Staff - Avg HVAC TotalBefore 7-30-13 - Unocc - Avg HVAC Total After 7-30-13 - Occ - Avg HVAC TotalAfter 7-30-13 - Staff - Avg HVAC Total After 7-30-13 - Unocc - Avg HVAC TotalBefore 7-30-13 - Occ - Average of Tout Before 7-30-13 - Staff - Average of ToutBefore 7-30-13 - Unocc - Average of Tout After 7-30-13 - Occ - Average of Tout
0
20
40
60
80
100
0
25
50
75
100
125
12
:00
AM
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10
:00
AM
12
:00
PM
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10
:00
PM
Ou
tdo
or
Tem
pe
ratu
re, d
eg
F
Avg
HV
AC
To
tal
Before 7-22-13 - Occ - Average of AC Total Before 7-22-13 - Staff - Average of AC TotalBefore 7-22-13 - Unocc - Average of AC Total After 7-22-13 - Occ - Average of AC TotalAfter 7-22-13 - Staff - Average of AC Total After 7-22-13 - Unocc - Average of AC TotalBefore 7-22-13 - Occ - Average of TOUT Before 7-22-13 - Staff - Average of TOUTBefore 7-22-13 - Unocc - Average of TOUT After 7-22-13 - Occ - Average of TOUTAfter 7-22-13 - Staff - Average of TOUT After 7-22-13 - Unocc - Average of TOUT
36
PG&E’s Emerging Technologies Program ET12PGE3461 FIGURE 6I. DAILY AVERAGE MONITORED HVAC USE AT ALAMEDA COUNTY FAST FOOD
RESTAURANT FACILITY, PRE & POST EMS
FIGURE 6J. DAILY AVERAGE MONITORED HVAC USE AT PLACER COUNTY FAST FOOD
RESTAURANT FACILITY, PRE & POST EMS
FIGURE 6K. DAILY AVERAGE MONITORED HVAC USE AT MERCED COUNTY FAST FOOD
RESTAURANT FACILITY, PRE & POST EMS
0
10
20
30
40
50
60
70
0
5
10
15
20
25
30
35
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Ou
tdo
or
Tem
pera
ture
, d
eg
F
Avg
HV
AC
To
tal
Before 1-8-14 - Occ - Average of HVAC Total Before 1-8-14 - Unocc - Average of HVAC Total
After 1-8-14 - Occ - Average of HVAC Total After 1-8-14 - Unocc - Average of HVAC Total
Before 1-8-14 - Occ - Average of TOUT Before 1-8-14 - Unocc - Average of TOUT
After 1-8-14 - Occ - Average of TOUT After 1-8-14 - Unocc - Average of TOUT
0
10
20
30
40
50
60
70
80
0
10
20
30
40
50
60
70
80
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Ou
tdo
or
Tem
pera
ture
, d
eg
F
Avg
HV
AC
To
tal
Before 10-24-2013 - Occ - Average of HVAC Total Before 10-24-2013 - Unocc - Average of HVAC Total
After 10-24-2013 - Occ - Average of HVAC Total After 10-24-2013 - Unocc - Average of HVAC Total
Before 10-24-2013 - Occ - Average of TOUT Before 10-24-2013 - Unocc - Average of TOUT
After 10-24-2013 - Occ - Average of TOUT After 10-24-2013 - Unocc - Average of TOUT
0
10
20
30
40
50
60
70
0
5
10
15
20
25
30
35
12:0
0 A
M
2:0
0 A
M
4:0
0 A
M
6:0
0 A
M
8:0
0 A
M
10:0
0 A
M
12:0
0 P
M
2:0
0 P
M
4:0
0 P
M
6:0
0 P
M
8:0
0 P
M
10:0
0 P
M
Ou
tdo
or
Tem
pera
ture
, d
eg
F
Avg
HV
AC
To
tal
Before 1-2-14 - Occ - Average of HVAC Total Before 1-2-14 - Unocc - Average of HVAC Total
After 1-2-14 - Occ - Average of HVAC Total After 1-2-14 - Unocc - Average of HVAC Total
Before 1-2-14 - Occ - Average of TOUT Before 1-2-14 - Unocc - Average of TOUT
After 1-2-14 - Occ - Average of TOUT After 1-2-14 - Unocc - Average of TOUT
37
PG&E’s Emerging Technologies Program ET12PGE3461
QUALIFICATIONS OF EVALUATION FINDINGS Several factors limit the ability to make broad conclusions from this study. These include the
small number of sites monitored (a common challenge among technology assessments), the
limited range of weather conditions monitored (with installation occurring in winter at many
sites), and some site anomalies and logger issues.
In all but three facilities, the monitoring took place during fall or winter conditions, with
average outdoor temperatures at just 60°F or lower. This prevented the evaluation of
potential savings during warm conditions, which demand more energy use for cooling. The
other three facilities (South Santa Clara County, San Joaquin County, and Placer County)
were monitored under warmer, summer conditions.
In addition, the HVAC energy data at three sites was problematic. In each case, very large
changes in HVAC energy use were measured, which were concluded to be beyond what the
EMS installation alone would cause. Data for one site was impacted by a rooftop HVAC unit
being disconnected and reconnected, and likely by other factors as well. For another site, it
appeared that the site was not occupied during the post-installation period (January at a
golf and country club). For these two sites, no HVAC energy savings were reported. Finally,
data for a third site indicated that errors were made in programming the EMS, shutting off
all HVAC at 11:00 am each day. For this site, HVAC savings during occupied hours were not
reported.
Some of the survey wording regarding temperature setpoints appears to have been mis
understood by respondents, leaving some uncertainty to exact setpoints used.
Finally, the study was slightly impacted by the failure of two data loggers at one site. And
the post-installation monitoring period for one site was only 11 days due to a delay in online
availability of data.
Future studies could address the above limitations, thereby increasing the size of the study
population and range of monitored weather conditions, and hopefully minimizing the relative
impact of site anomalies and logger issues on study results.
PRE AND POST OCCUPANT BEHAVIOR Energy savings achieved through installation of the small commercial EMS depends on the
behavior of users. Incomplete customer understanding of features and functions can
adversely impact energy savings. The EMS provides the functionality that allows users to
save energy by setting optimum scheduling and set points, but it takes proper training and
follow-up to realize maximum savings. For example, two users commented that the EMS did
not shut systems off for holidays (see Appendix C). Yet the EMS does provide holiday
scheduling functionality.
This section summarizes the changes in scheduling and setpoints implemented by users
through the installation of the EMS.
We compared operating hours as reported in the pre-surveys to the operating hours actually
scheduled in the EMS. See Table 11 on the following page.
38
PG&E’s Emerging Technologies Program ET12PGE3461
TABLE 11. WEEKLY OPERATING HOUR COMPARISON
Pre-Installation
Survey
Reported Hours
EMS
Scheduled Hours
Change
Occupied Hours 87.3 88.5 +1.2 hours
(+1.4%)
Staff Hours 35.8 6.7 -29.1 hours
(-81%)
Unoccupied Hours 44.9 72.8 +27.9 hours
(+62%)
Totals 168 168 0
For regular occupied hours, there is very good agreement between operating hours as
reported in the survey, and what was programmed in the EMS. However, for sites where
there are staff hours (periods when only staff are present in the facility), many of these
hours were actually programmed with more aggressive unoccupied hour settings in the
EMS. In other words, for most staff hours, the HVAC was programmed off. This means that
the schedules implemented in the EMS are reducing the amount of HVAC use during hours
when only staff is in the facility.
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PG&E’s Emerging Technologies Program ET12PGE3461
We also compared temperature setpoints used before and after EMS installation. Figure 7 on
the following page shows the average cooling and heating setpoints used in facilities pre-
and post-installation of the EMS. Pre-install setpoints were derived from the values reported
in facility manager surveys3. Post-install setpoints were confirmed by analysis of the EMS
online website for each facility.
Cooling setpoints were increased an average of 2°F during occupied periods, and increased
by less than 1°F during unoccupied periods. Heating setpoints were decreased an average
of about 1.5°F for both occupied and unoccupied periods. These setpoint changes are
drivers of energy savings at these facilities.
FIGURE 7. COOLING AND HEATING SETPOINTS DURING OCCUPIED AND UNOCCUPIED PERIODS PRE- AND POST- EMS
INSTALLATION
While the figure above presents averages for all sites, the following table shows the full
detail of setpoints for each site.
3 Some reported setpoints were considered incorrect and were removed from the average
calculation. Specifically, cooling setpoints lower than heating setpoints and vice versa.
70.872.3
62.3
80.6
69.2
74.3
61.0
81.2
45
50
55
60
65
70
75
80
85
OccupiedHeating
OccupiedCooling
UNoccupied Heating UNoccupied Cooling
Pre setpoints Post setpoints
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PG&E’s Emerging Technologies Program ET12PGE3461
TABLE 12. COMPARISON OF TEMPERATURE SETPOINTS, REPORTED PRE-INSTALL AND EMS POST-INSTALL
Finally, we also compared actual setpoints entered in the EMS with reported setpoints
provided in the post-installation survey. These results may have been affected by somewhat
confusing wording in the survey. About one-third of survey respondents correctly listed all
four setpoints as they were entered into their EMS. Another one-third of respondents
correctly listed most setpoints, and about one-third incorrectly listed most setpoints. Note
that about half of post survey respondents reported that building occupants can adjust
these setpoints.
COOLING HEATING
Occupied Hrs Unoccupied Hrs Staff Hrs Occupied Hrs Unoccupied Hrs Staff Hrs
Location Building Type
'Before'
Report EMS D
'Before'
Report EMS D EMS
'Before'
Report EMS D
'Before'
Report EMS D EMS
Kern County Retail Clinic 72 73 1 85 85 0 0 72 70 2 68 65 3 0
Alameda
CountyRetail 74 76 2 80 80 0 0 73 72 1 55 62 -7 0
Nevada
CountyAssembly 72 74 2 50 80 30 0 68 70 -2 50 55 -5 0
Lake County Assembly 73 75 2 90 80 -10 0 70 71 -1 50 55 -5 0
Stanislaus
County
Assembly /
Restaurant72 75 3 78 80 2 0 72 72 0 78 65 13 0
Santa Clara
County
Assembly /
Restaurant72 74 2 80 78 -2 0 68 68 0 60 65 -5 0
Santa Clara
County
Sit Down
Restaurant72 75 3 82 85 3 78 70 67 3 60 55 5 65
San Joaquin
County
Sit Down
Restaurant72 75 3 82 85 3 78 70 67 3 60 55 5 65
Alameda
County
Fast Food
Restaurant72 74 2 74 80 6 0 72 68 4 72 65 7 0
Placer
County
Fast Food
Restaurant72 72 0 60 80 20 0 72 68 4 60 64 -4 0
Merced
County
Fast Food
Restaurant72 74 2 74 80 6 78 72 68 4 72 65 7 66
AVERAGE 72.3 74.3 2.0 80.6 81.2 0.9 21.3 70.8 69.2 1.6 62.3 61.0 1.3 17.8
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PG&E’s Emerging Technologies Program ET12PGE3461
SURVEY HIGHLIGHTS
PRE-INSTALLATION SURVEYS
All 15 responding sites reported that HVAC controls featured programmable thermostats
before the EMS was installed. Four sites had a single thermostat; 11 sites had multiple,
independent programmable thermostats. Just over half (8 sites) reported that tenants could
adjust or override programmed temperature settings, with 11 sites reporting that this
happens routinely including 5 sites where adjustments occur at least daily.
Regarding lighting control, 40% of sites reported that building management or the store
manager controls the lights, 60% said tenants control the lights, while one site reported
control by employees. (One site reported both management and tenant control.) Eighty
percent of reporting sites noted having photocell or timer control of exterior lighting, while
three sites (20%) reported having occupancy sensors for specific areas (exam rooms,
storage areas).
POST-INSTALLATION SURVEYS
All responding facilities reported that no changes had occurred to:
facility operating hours,
the number of employees,
the number of customers, or
to lighting and HVAC systems.
Only one site reported different light levels used through the day (using a dimmer for
special events).
Only two sites reported receiving alerts, which were reported as false alarms.
Respondents were asked to list EMS pros and cons. Their comments are shown below under
“Appendix C: Post-Installation Survey Responses.”
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PG&E’s Emerging Technologies Program ET12PGE3461
EVALUATIONS Based on our study of eleven facilities that participated in the study, the implementation of
the EMS is an effective measure for saving energy in small commercial buildings. The EMS
helped facilities save energy in the following ways:
1. During the installation and setup process, facility managers tended to adjust their
operating ‘occupied’ hours conservatively downward, especially during hours when
only staff is in the facility. This scheduling improvement was the driver of significant
but highly variable energy savings. These improvements and resultant savings
occurred despite all sites already having programmable thermostats before the EMS
was installed.
2. Facility managers reviewed and standardized HVAC setpoints during the installation
process, and instituted higher cooling setpoints and lower heating setpoints,
especially for occupied periods. In addition, they indicated that the central control
(and therefore persistence of) setpoints was a major advantage of the EMS. Both the
more conservative setpoints and their central control and persistence are additional
drivers of energy savings.
3. Facility managers implemented lighting schedules that were automatically controlled
and not dependent on daily behavior by individuals.
In addition, we also noted that the post-installation survey feedback about the EMS was
very positive, with users reporting few or no drawbacks.
Two users reported that the system did not shut down during holidays; however holiday
scheduling functionality is available for users to set-up. This highlights the importance of
thorough user training and follow-up to help ensure that all available energy-saving
functionalities are put to use by users.
The version of the EMS product studied offered a 15 minute demand trending function
through an optional firmware upgrade to the on-site touchscreen device. The data is
available via an engineering database tool and is also available via the customer facing web
application. Power monitoring, including interval data reporting, is an important feature to
help increase the energy savings achieved with a small commercial EMS, as such usage
trends clearly indicate the impact of scheduling changes, helping users to understand and
control energy use. Unfortunately this functionality was not evaluated as part of this study,
as it was not evident to the reviewer despite an extensive review of the product
documentation made available by the provider.
Overall, the EMS functioned mostly as expected. It is clear that the major differentiator of
this type of product is in the software interface and vendor support of implementation. The
hardware is fairly straightforward; the success of the system depends mainly on making it
easy for users to implement control changes via the software interface.
The following section provides background and suggestions regarding the potential to offer
efficiency program incentives, including hybrid-deemed incentives, to encourage both the
installation of small commercial EMS (and similar controls improvements) and
implementation of energy-savings functionalities enabled by such devices.
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PG&E’s Emerging Technologies Program ET12PGE3461
POTENTIAL FOR EFFICIENCY PROGRAM INCENTIVES
INTRODUCTION Small commercial EMS are one of many new building control devices and techniques
entering the market which claim to save energy. These control devices and techniques do
not inherently save energy. They enable new control functionality which can save energy.
This complexity provides challenges to developing incentives.
This section explores the suitability of using efficiency program incentives, including hybrid-
deemed incentive approaches, to encourage the adoption of control retrofits (such as small
commercial EMS) to save energy, including challenges in establishing baseline conditions,
verifying installed results, and estimating savings.
DEFINITIONS Following are key definitions which are particular to this section.
New Functionality (NF) – A New Functionality is an improvement to controls
capabilities or sequences of operations utilizing new control or signal response
logic. Controls measures such as small commercial EMS provide New
Functionalities, which can enable more efficient operation of HVAC and lighting
equipment -- yielding long-term, quantifiable energy savings. These New
Functionalities become possible when suitable Supporting Technology is installed,
such as a small commercial EMS which enables scheduling, setbacks, setpoint
enforcement and similar New Functionalities.
Supporting Technology (ST) - A controls Supporting Technology (ST) is a product
that provides additional signal response or control capability to the existing
equipment or control system. The example here is the small commercial EMS
control equipment. By design, it is used to provide a New Functionality that is not
inherently available with the existing equipment or control system.
Deemed Incentives - In their simplest form, deemed incentives are cash
payments that are given for replacing old equipment with new, energy efficient
equipment. The new equipment simply draws less power than the old equipment,
needing minimal adjustment or set up, thus directly saving energy whenever it
operates. Generalized per-unit energy and demand savings estimates are
calculated for deemed measures by making reasonable assumptions about the
typical equipment being replaced and its normal operating hours.
Deemed incentives can also be used for measures that help reduce the energy
use of existing facilities and equipment, like the installation of variable frequency
drives or window films. For these more complicated measures, deemed savings
can be dependent upon the climate zone, and the type and vintage of the
building where they are installed. Deemed incentives are largely determined
based on how much these savings are worth to the utility’s energy portfolio.
Deemed incentives provide a straightforward, low-cost approach to incentivizing
simple efficiency projects where typical savings can be reasonably developed.
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PG&E’s Emerging Technologies Program ET12PGE3461
Custom Incentives - Custom incentives are given on a case-by-case basis for
efficiency measures that are not common, or that depend heavily on the
conditions of their application. Incentives are based on the amount of energy
saved, and savings are determined by program participants based on engineering
calculations or building energy modeling. Custom incentives demand a higher
level of effort from both program participants and program implementers. This
makes them more expensive and difficult for programs to administer.
Hybrid-Deemed Incentives - In this section, “hybrid-deemed incentives” are a
hybrid of traditional deemed and custom program incentives. Hybrid-deemed
incentives are defined in this section as fundamentally deemed in nature, but add
custom aspects as needed. The added aspects may be needed in order to
adequately establish which projects are eligible (baseline conditions), to verify
installed results, or to provide reasonable, justifiable estimates of energy savings.
Regarding savings estimates, for example, a hybrid-deemed incentive may
provide a deemed incentive per unit, adjusted to key particulars of the
application. Particulars determining the assigned savings might include, for
example, climate zone, building type, base case equipment, base case settings,
or post case settings.
CUSTOM INCENTIVE MEASURES Custom incentives can be applied to some installations of small commercial EMS. Individual
projects could be enrolled in existing custom retrofit incentive programs. Once enrolled and
confirmed eligible, the process would likely be similar to the retrofit isolation analyses
completed at 11 sites for this study. The process may include a consultant to the utility first
visiting the site to confirm base case conditions and eligibility, and to install temporary
logging equipment. After project installation, the consultant would return to the site to
verify system installation and to collect data loggers. An analysis of the logged data would
include extrapolation to estimate annual savings achieved. Documentation of actual project
costs would be collected. Payment would then be made to the customer based on set rates
of dollars ($) per units of electricity and gas energy saved and peak demand reduction, if
any.
Baseline Considerations
Under current eligibility rules, the addition of controls alone qualifies as a retrofit add-on
project type. As such, the baseline is the existing system and incentives apply to actual
savings. However, code baseline applies if there was also an equipment replacement and
the existing HVAC or lighting equipment is past its corresponding DEER equipment useful
life (EUL). In that case, incentives apply to only savings beyond code minimum. More
critically, in cases with existing programmable thermostats (which includes all eleven sites
in this study), any HVAC savings is likely to be disallowed by existing programs because the
basic functionality to schedule setbacks already exists.
In addition, recent CPUC direction regarding analysis of lighting savings for incentive
purposes does not allow existing operating hours to be used for the baseline - DEER
standard hours would be taken as baseline for that building type. So savings due to excess
run hours in the pre-installation case would not be eligible for incentives.
Opportunities
The custom incentive approach provides the most accuracy in determining project-specific
verified savings. In turn, this approach pays the most incentives to where savings are
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PG&E’s Emerging Technologies Program ET12PGE3461
highest. For the small commercial EMS measure, savings vary significantly by site as
demonstrated by this study.
Challenges
Recent issues could impact custom incentives for some small commercial EMS installations.
Baseline requirements are evolving which causes confusion in the market. In addition,
current programs now have minimum project size requirements – for PG&E, eligible projects
must have savings that earn at least $2,000 in incentives to receive detailed analysis and
verification.
Generally, the custom incentive approach is the most complex, time-consuming, and costly
approach for both the utility and the customer. Customers may be discouraged from
undertaking projects and/or from applying for custom incentives due to the process and
uncertainty involved. For small commercial EMS, the costs of custom, project-specific
savings analysis are high relative to the cost of the project itself.
DEEMED INCENTIVE MEASURES Deemed incentives (rebates) could be applied to installations of small commercial EMS. With
this deemed approach, a “catalog” of rebates lists a fixed rebate value per project. A
customer submits a rebate request providing only an invoice or related documentation
confirming purchase of equipment.
For the small commercial EMS measure, a tiered deemed approach would be appropriate,
with savings credits and/or rebate payments depending on some project specifics. For
example, deemed rebates could be tiered by:
site scale (e.g. square footage, number of thermostats or lighting circuits
controlled),
site type (e.g. restaurant, assembly), and/or
climate zone.
A workpaper would need to be developed which reasonably indicates typical savings for
each sub-category. The data and conclusion from this study would provide a start to this
workpaper.
Opportunities
The deemed incentive approach provides the simplest path for both utility and customer.
The process is simple for the customer and there is little to no uncertainty in the incentive
payment to be paid and received. The cost and time required by both utility and customer is
low, so that more funding is dedicated to incentive payments rather than program
administration costs. At the program level, total savings achieved is expected to average
out individual high- and low-savings sites.
Challenges
The deemed incentive approach provides little differentiation of savings or incentives paid
by project. Incentives are paid simply based on hardware installed, without consideration of
actual pre- or post-case operating conditions, which impact achieved savings significantly as
shown in this study.
No data is collected through the deemed incentive program itself to indicate actual savings
achieved.
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PG&E’s Emerging Technologies Program ET12PGE3461
Program-level costs are incurred in developing quality workpapers supporting claimed
savings from installed projects.
Ongoing changes to baseline requirements could impact the ability to establish workpapers
supporting deemed savings estimates.
HYBRID-DEEMED INCENTIVE MEASURES Hybrid-deemed incentive measures could be effective in effecting resource acquisition and
market transformation. The hypothesis is that hybrid-deemed incentive programs for
controls projects such as small commercial EMS can be developed which are simpler, faster,
and less costly than customized incentives, while still providing reliable estimates of energy
savings in aggregate.
There are challenges to developing hybrid-deemed incentives for controls retrofits such as
small commercial EMS. We have grouped the primary issues into three categories:
1) Determining Eligibility (Baseline Conditions)
2) Estimating Savings
3) Verifying Savings (Post-case Confirmation)
The following discussion highlights the key differences of hybrid-deemed controls measures
from traditional deemed catalog measures. Specific discussion regarding installation of small
commercial EMS is provided later in this section.
Does the hardware directly save energy?
Traditional deemed measures normally involve new hardware additions or replacements
which result in energy savings. For example, installation of a more-efficient boiler saves gas
directly. Another example is an occupancy sensor which itself shuts off lights to save
energy.
With controls improvements like an EMS, new hardware alone does not directly save
energy. The new hardware must be correctly implemented to enable new, improved
controls, which in turn save energy. Specifically, when installing a small commercial EMS,
savings are achieved by using the new capabilities to reliably implement and maintain
equipment scheduling and setbacks.
Eligibility (Baseline Conditions)
With traditional deemed measures, the baseline conditions for eligibility are made by
common-sense assumptions about the pre-hardware condition, or by surveys to determine
typical marketplace or facility conditions. For example, the baseline condition for a new
boiler would be a minimally-efficient boiler available for purchase today (or the efficiency of
the old boiler, in the case of an early retirement project).
With hybrid-deemed controls measures, the baseline conditions may need to be reviewed
for eligibility. It is possible that the control’s New Functionality could have been
implemented with the pre-existing equipment, which may be considered to make the
measure ineligible. For example, existing programmable thermostats may be capable of
applying schedules and setbacks. Installation of an EMS may not be strictly needed to
implement this energy-saving strategy. (However this study demonstrates that an EMS does
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PG&E’s Emerging Technologies Program ET12PGE3461
greatly improve the likelihood that scheduling and setback strategies are consistently and
reliably implemented, compared to individual programmable thermostats.)
Conversely, in another example the existing control system may include regular thermostats
not capable of scheduling temperature setbacks, but the staff reliably turned the thermostat
up and down by hand. In this case the new control system may save labor, but may not
save any energy.
Verification (Post-case Confirmation)
With traditional deemed measures, the purchase of new hardware is implicitly assumed to
mean that the hardware was installed and produced energy savings. There is no verification
process beyond a check of purchase documentation (i.e. invoices or receipts for hardware),
and perhaps visits to a sample of sites to make sure the hardware has been installed and is
functioning.
With hybrid-deemed controls measures, the purchase and installation of new hardware does
not necessarily indicate that new controls functionality was implemented or energy savings
were achieved. Some additional verification of operating characteristics will likely be
appropriate. The type of verification needed depends on the measure.
Savings Estimates
Controls improvements currently require that custom savings estimates be made for each
installation. This custom treatment is time-consuming and costly, requiring both detailed
understanding of the particular application and (often) extensive data logging and analysis.
For specific controls measures enabled by new technologies, the hypothesis of this section is
that methods of estimating savings can be developed which are simpler, faster, and less
costly – while still providing reasonable estimates of energy savings in aggregate.
A hybrid-deemed incentive approach needs to accurately estimate the energy savings of
typical measures and typical installations, in order to provide acceptable estimates of
program-level savings. While the resulting hybrid-deemed savings methodology is not
expected to provide the same level of project-level precision provided by customized
incentive procedures, it can improve customer experience, and reduce the time and expense
of developing site-specific energy savings estimates. Savings can be estimated using an
approach which applies building modeling to a range of scenarios (e.g. building type,
vintage, climate type), based on California Database for Energy Efficiency (CA DEER)
prototypical building models. Results from modeling are then regressed to give correlations
that can be used to more easily estimate savings. This approach was demonstrated in a
previous PG&E Emerging Technologies Program study4. That study used wireless pneumatic
thermostats as an example technology enabling multiple new functionalities. The regression
correlations were based on independent variables that are not only statistically significant,
4 Hybrid Deemed Incentive Methodology for HVAC Control Retrofits, ET Project Number
ET11PGE5172, 2013
(http://www.etcc-ca.com/reports/hybrid-deemed-incentive-methodology-hvac-control-
retrofits)
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but have engineering significance in terms of their influence on energy use. Regression
correlations like those developed in that study could be used by incentive programs to
estimate savings of proposed projects. Protocols based on these regressions were developed
which incentive programs can use to determine fixed incentives for installing controls
technologies, plus additional incentives for correctly enabling various functionalities.
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SCREENING PROJECTS FOR ELIGIBILITY AND VERIFICATION This section presents ways in which hybrid-deemed incentive programs could screen
supporting technologies and new functionalities for proposed control measures, in order to
check their eligibility and to verify effective implementation.
SUPPORTING TECHNOLOGIES
The Supporting Technologies (ST) for controls measures are relatively straightforward to
verify because they involve the installation of new hardware whose physical presence can
be readily documented. However, confirmation of eligibility can be more challenging, since
the capabilities of existing baseline controls equipment are often not obvious. Verification of
effective implementation for many of these technologies must rely on competent set-up or
commissioning of these systems. Please see Table 12 below listing only small commercial
EMS, and (similar) programmable communicating thermostats (PCTs). Due to the
proliferation of smartphones, photos can be quite easily submitted by customers.
TABLE 13: POTENTIAL INFORMATION TO COLLECT TO DETERMINE THE ELIGIBILITY AND VERIFY EFFECTIVE OPERATION OF
SUPPORTING TECHNOLOGIES
Supporting
Technologies, ST
Eligibility / Baseline Confirmation Post-Install Verification
ST1 –
Wireless Unitary
EMS
Photos of existing thermostats.
Photos of existing time clocks or
lighting control panels for time-
run systems.
Invoice for new system hardware.
Screen shots of at least 3 screens,
including main control screen and
two trends.
Additional screen shots to show
implementation of NF measures.
Consider requiring trending
capabilities for new controls.
New control sequences are likely to
have been implemented as part of
this improvement. Remote review of
trends will ensure sequences have
been implemented properly.
ST2 –
Programmable
Communicating
Thermostats
(PCT)
Photos of the existing
thermostats.
Invoice and photos of the new
thermostats.
Vendors may have documentation of
verification/commissioning of
systems.
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PG&E’s Emerging Technologies Program ET12PGE3461
NEW FUNCTIONALITIES
The New Functionalities for controls measures are more difficult to verify than the more
straightforward installation of hardware. Additional information will be needed about
control system hardware and software. Trend data from the control system can also serve
to verify correct operation and energy savings. Please see Table 13 below.
TABLE 14: POTENTIAL INFORMATION NEEDED TO DETERMINE THE ELIGIBILITY AND VERIFY EFFECTIVE OPERATION OF NEW
FUNCTIONALITIES
New
Functionalities,
NF
Eligibility / Baseline Confirmation Post-Install Verification
NF 1 – Set Point
Enforcement5
Photographs of existing
thermostats with local user
controls.
Invoice and photo of new
thermostat showing set points.
NF 2 – Setback /
Setup /
Scheduling
Photographs of existing
thermostats with local user
controls and schedules.
Invoice and photo of new EMS
screen, showing different scheduled
settings.
NF 3 – Dead
Band
Cooling/Heating
Photographs of existing
thermostats showing single
temperature setpoint (no
deadband between heating and
cooling).
Sequence of operations and screen
shot of trend (cooling & heating
energy use versus zone
temperatures) or 1 day of trend
data (cooling & heating energy use
over time).
NF 4 – Optimum
Start/Stop (OSS)
Photographs of existing
thermostats will be evidence that
this capability is not in place.
Cut sheet of controller with
indication that it has OSS capability
and screen shot of at least 1 day of
trend data (heating and cooling
energy use and zone temperatures
around the beginning and end of
occupied periods).
NF 5 – Global
Temperature
Optimization
Photographs of existing
thermostats will be evidence that
this capability is not in place.
Sequence of operations and screen
shot of at least 1 day of trend data
for at least 10 zones (zone
temperatures over time).
5 Centralized control of set points. Reset overridden set points after a certain amount of
time.
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EMS CONTROLS: FIXED INCENTIVE AND FUNCTIONALITY
INCENTIVES Instead of providing a single incentive based on estimated savings of enabled functionalities
(e.g. scheduling, setbacks, setpoint enforcement, deadband, optimum start), an incentive
program may want to split incentives into two parts:
1. A fixed incentive for installation of a supporting technology (such as a small
commercial EMS or similar).
2. Additional incentive(s) that depend on the functionalities enabled (i.e. scheduling,
setbacks, setpoint enforcement, etc.).
The fixed incentive helps defray the costs for utility customers to install new controls
technologies such as small commercial EMS. Fixed incentives can be a particularly attractive
stimulus for smaller customers with limited financial resources. Additional incentives can be
offered to customers who take the time and effort to make sure their controls are
completely and properly enabled.
We propose that the fixed incentive be tied to savings of a specific baseline functionality.
The baseline functionality can be chosen for each supporting technology. Baseline
functionalities should meet the following criteria:
Commonly or easily enabled by a new technology,
Expected to deliver savings across all building types, vintages, and climate zones,
Savings estimates are based on conservative assumptions.
For small commercial EMS specifically, fixed incentives could be tied to expected savings
due to scheduling improvement. Since this technology enables relatively few and simple
new functionalities, a dual incentive may not be needed.
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RECOMMENDATIONS This study confirmed that installing a small commercial EMS results in generally significant
but highly variable energy savings for both HVAC and lighting systems. This result is typical
of control system improvements generally, where new hardware (Supporting Technology)
enables but does not guarantee improvements to operations and controls (new
functionalities) which provide energy savings. Achieved savings depend on both the existing
controls, and on how the new technology is put to use.
We recommend that incentives be developed for small commercial EMS and similar controls
improvements. Specifically we show in this report a proposed ‘hybrid-deemed’ approach
which is fundamentally deemed in nature, but with limited additional procedures for each
application to confirm baseline conditions for purposes of eligibility and savings
determination, and for post-install verification including both technology and functional
improvements. We show that savings could be determined using an approach which applies
building modelling based on DEER prototypical models, and then applies regression
correlation to the modelling results. This method provides low-cost standardized savings
results which are dependent on significant and readily-identified site specifics such as
climate zone, building type and vintage).
We recommend that hybrid-deemed incentives be developed which apply to a range of
controls products of various types, based on the functionalities those products enable.
Related simpler products offer a part of the functionality enabled by the small commercial
EMS studied here. As opposed to integrating controls for HVAC, lighting, refrigeration as
well as power monitoring into one system, much of the energy savings might be achieved
with simpler systems focused on the greatest opportunities. Since average savings in this
study were much higher for HVAC than for lighting, HVAC-specific controls measures may
be most cost-effective in these types of sites. For example, a variety of programmable
communicating thermostats (PCT) are available. These PCT’s are typically wireless with
centralized control and feedback. One of the major differentiators for these products is in
software. Wireless thermostat products tend to focus their marketing and functionality on
specific markets, with significant overlap between residential and small commercial
markets. There is a large and growing array of manufacturers of wireless PCTs.
Finally, we see an opportunity to expand the functionality of the EMS product to include
improved power monitoring data reporting and trending. Specifically, interval (e.g. hourly)
usage data and trends could be made available to users for more than one day.
POTENTIAL NEXT STEPS
COMPARISON OF SAVINGS WITH EXISTING MODELING RESULTS
In this study we measured actual savings results for a range of building types and climate
zones within PG&E service territory. We also already have estimated saving values (per
square foot) based on DEER model results for related measures (scheduling/setback,
setpoint enforcement and others) at four building types (small and large retail, small and
large office), 3 building vintages and 9 climate zones. The measured results could be
compared with the modeling results, to provide an initial indication of the validity of the
modeling approach.
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EXPAND AND DEVELOP INCENTIVE APPROACH AND TOOLS
We recommend proceeding with some next steps in preparation for launching a pilot hybrid-
deemed incentive program to promote the adoption of controls technologies including small
commercial EMS. Next steps to pursue would include:
Develop an easily usable and robust energy estimating spreadsheet tool based on
regressed modelling results. The spreadsheet tool can be expanded upon to add
more supporting technologies and their functionalities, as these are analyzed using
modeling and regression analysis.
Model additional functionalities and building types, and make additional runs using a
wider range of building characteristics in order to expand the set of energy savings
estimates. In particular, California schools are an important market where improved
controls will be installed statewide over the next few years using Proposition 39
funding.
Research and document the costs and functionalities of related controls technologies
available today. Prepare a database of available controls products, their primary
application targets, and the functionalities they can enable. Especially with the
advent of inexpensive wireless technologies, the controls field is undergoing rapid
development. Further research is needed to determine more clearly what
technologies are available, which functionalities they can enable, and what the costs
are to purchase, install, and commission them. This information is important input
for determining program incentives.
Determine program incentive structures based on energy savings, measure costs,
including any caps on customer incentives.
CONSIDERATIONS FOR FUTURE FIELD STUDIES This study suggests that energy savings were generally significant. However the study
sample also yielded a wide variation of savings results. Such variability is expected,
especially for small commercial facilities which can vary widely in their energy use patterns
given their high diversity of behavior and building use, existing control systems, building
and system types, and climate.
That said, additional field studies could help to clarify the energy savings opportunities for
this technology set in small commercial buildings. Sponsors of future studies would benefit
from incorporating some of the lessons learned from this study, specifically:
Record complete baseline information at studied sites, to help with confirmation
results from raw energy use data. For example,
o Setpoints and schedules in existing programmable thermostats should be
checked and recorded, rather than relying solely on customer survey self-
reporting.
o Any unusual conditions (such as an HVAC switched off) should be investigated
further to understand any consequences for the study.
o General photos and documentation of baseline conditions should be recorded
to allow confirmation that no significant site changes were made.
Use clear and consistent wording in customer surveys to avoid ambiguity and
improve the usability of survey feedback.
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Collect data through as wide a range of weather as possible.
Check and confirm any changes in building occupancy or use between the pre and
post-installation periods.
Consider installing occupancy loggers at doors to record occupant first entrance and
final exit, as appropriate to the site.
Seek to have sites leverage the refrigeration monitoring and control capabilities as
well, to demonstrate the full potential for savings.
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APPENDICES
APPENDIX A: PRE-INSTALLATION SURVEY PG&E Energy Management System (Pre-Installation Survey)
Please identify yourself and the location for which you will be answering these questions.
Your name: _______________________
Facility name: _______________________
Your role: _______________________
Address line 1: _______________________
Address line 2: _______________________
City, ST, ZIP: _______________________
G1. What are the standard facility operating hours? Be sure to specify AM or PM.
G2. Do any people come in early or stay late beyond the normal operating hours?
1. Yes 2. No – Go to G3.
If G2 = Yes
G2a. Outside of normal operating hours, when is the earliest personnel would come in?
G2b. Outside of normal operating hours, when is the latest personnel would leave?
G3. Is maintenance or cleaning done on a regular basis outside of normal operating hours?
G4. How many people work at this facility? ____
G5a. How many customers visit this facility daily on weekdays? ____
G5b. How many customers visit this facility daily on weekends? ____
The next few questions are about the lighting at your facility.
L1. Who controls the lights? Please select all that apply.
Open 24/7 Closed Open Time Close Time
Weekdays g1a1 g1a2 g1a3 g1a4
Weekends/Holidays g1b1 g1b2 g1b3 g1b4
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1. Building management
2. Individual tenants
3. Other - Specify ______________________
L2. When are lights turned on and off on weekdays and weekends? Be sure to specify AM or PM.
L3. Are there different sets of lights (interior, exterior, other) that are used at different times of day?
1. Yes 2. No
L4. Which of the following does your facility have? (Check all that apply)
Occupancy Sensors Daylight Sensors Photocells Timers Dimmers None of the above – Go to L6.
For those that were selected:
L5. For each one, please list the type of space that it controls. For instance, some customers use photocells to control exterior lighting or dimmers to control conference room lighting.
What does it control?
Occupancy Sensors
Daylight Sensors
Photocells
Timers
Dimmers
L6. In your opinion, what are some of the biggest problems, challenges, or concerns, if any, with your current lighting system operation? ________________________________________________
On 24/7 Facility Closed Turned on at Turned off at
Weekdays l2a1 l2a2 l2a3 l2a4
Weekends/Holidays l2b1 l2b2 l2b3 l2b4
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The next few questions are about the HVAC (Heating, Ventilation and Air-conditioning) system at your facility.
H1. Which of the following describe how the temperatures at your facility are being controlled? 1. A single programmable thermostat 2. Multiple programmable thermostats 3. A single manual thermostat – Go to H5 4. Multiple manual thermostats – Go to H5 5. On-site control system 6. Control system operated off-site 7. Control system operated via the internet
If H1 < 3
H2. Are the programmable thermostat(s) programmed to automatically adjust the temperatures on different days or at different times?
1. Yes – Go to H5 2. No – Go to H5
If H1 > 4
H3. Does the same system control both the lighting and HVAC systems?
1. Yes 2. No
H4. Who originally programmed this system?
1. The original engineers who installed the system 2. The facilities manager 3. Other - Specify ______________________
H5. Who operates this system today, and makes changes if they’re needed? ______________________
H6. Can tenants adjust or override the programmed temperature settings?
1. Yes 2. No – Go to H7
H6a. What limits are there to the temperature adjustments that can be made by the tenants?
1. None – they can adjust them at will 2. Can only raise or lower by 4 degrees Fahrenheit 3. Can only raise or lower by 2 degrees Fahrenheit 4. They can’t change them at all 5. Other - Specify ______________________
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H7. How often are temperature settings adjusted?
1. Never – Go to H9 2. Once a month – Go to H9 3. Once a week – Go to H9 4. Once a day 5. 2-3 times a day 6. More than 3 times a day
H8. How many days a week are temperatures adjusted for the following?
The space is too hot h8a1
The space is too cold h8a2
Beginning of day h8a3
End of day h8a4
Other- h8a5
-Specify h8a5s
H9. Please list the typical temperature settings when the building is OCCUPIED and when it is UNOCCUPIED for both summer and winter.
H10. In your opinion, what are some of the biggest problems, challenges, or concerns, if any, with space temperatures in your building?
1. Poor comfort 2. Hot or cold areas 3. Hard to adjust 4. Too many people can control the temperatures 5. Adjusting during unoccupied hours 6. Other – specify ____________________________________________
H11. How often is your HVAC unit serviced?
1. Monthly 2. Every three months 3. Every six months 4. Every year
Temperature when
Occupied
Temperature when
Unoccupied
Summer h9a1 h9a2
Winter h9b1 h9b2
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5. Other – specify ______________________________
H12. Who performs the servicing?
1. Outside HVAC contractor 2. In-house maintenance employee 3. Other – specify ______________________________
This last section is about other equipment in your building (besides lighting and HVAC) that uses a lot of energy, either natural gas or electricity.
O1. Please review the list of equipment below and check the ones that you feel use a lot of energy (natural gas or electricity) in your building.
For those that were selected: O2. Who controls this equipment?
On-site facility
manager
Asset/ or Property manager
Office Manager
Individual Users
o1a Gas cooking equipment o2a1 o2a2 o2a3 o2a4
o1b Electric cooking equipment o2b1 o2b2 o2b3 o2b4
o1c Appliances o2c1 o2c2 o2c3 o2c4
o1d Refrigeration o2d1 o2d2 o2d3 o2d4
o1e Ventilation o2e1 o2e2 o2e3 o2e4
o1f Gas water heating o2f1 o2f2 o2f3 o2f4
o1g Electronic office or computer equipment o2g1 o2g2 o2g3 o2g4
o1h Electric water heating o2h1 o2h2 o2h3 o2h4
o1i Fork lift battery charger o2i1 o2i2 o2i3 o2i4
o1j Space heaters o2j1 o2j2 o2j3 o2j4
o1k Other – specify 1 o1ks o2k1 o2k2 o2k3 o2k4
o1l Other – specify 2 o1ls o2l1 o2l2 o2l3 o2l4
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For those that were selected:
O3. When is this equipment turned on and off? Be sure to specify AM or PM.
1 = Always on 2 = On during biz hours
Time(s) turned on
Time(s) turned off
Gas cooking equipment o3a1 o3a2 o3a3
Electric cooking equipment o3b1 o3b2 o3b3
Appliances o3c1 o3c2 o3c3
Refrigeration o3d1 o3d2 o3d3
Ventilation o3e1 o3e2 o3e3
Gas water heating o3f1 o3f2 o3f3
Electronic office or computer equipment o3g1 o3g2 o3g3
Electric water heating o3h1 o3h2 o3h3
Fork lift battery charger o3i1 o3i2 o3i3
Space heaters o3j1 o3j2 o3j3
Other – specify 1 _______________________ o3k1 o3k2 o3k3
Other – specify 2 _______________________ o3l1 o3l2 o3l3
For those that were selected:
O4. Which of these requires warm-up, cool-down or standby time? (Check all that apply)
Warm-up required
Cool-down required
Standby required
None required
Gas cooking equipment o4a1 o4a2 o4a3 o4a4
Electric cooking equipment o4b1 o4b2 o4b3 o4b4
Appliances o4c1 o4c2 o4c3 o4c4
Refrigeration o4d1 o4d2 o4d3 o4d4
Ventilation o4e1 o4e2 o4e3 o4e4
Gas water heating o4f1 o4f2 o4f3 o4f4
Electronic office or computer equipment o4g1 o4g2 o4g3 o4g4
Electric water heating o4h1 o4h2 o4h3 o4h4
Fork lift battery charger o4i1 o4i2 o4i3 o4i4
Space heaters o4j1 o4j2 o4j3 o4j4
Other – specify 1 _______________________ o4k1 o4k2 o4k3 o4k4
Other – specify 2 _______________________ o4l1 o4l2 o4l3 o4l4
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For those that were selected:
O5. Which of these stay running when not in use? (Check all that apply)
o5a Gas cooking equipment
o5b Electric cooking equipment
o5c Appliances
o5d Refrigeration
o5e Ventilation
o5f Gas water heating
o5g Electronic office or computer equipment
o5h Electric water heating
o5i Fork lift battery charger
o5j Space heaters
o5k Other – specify 1
o5l Other – specify 2
Please provide the following information for the $50 thank-you check.
Name on the check: _______________________
c/o (if applicable): _______________________
Address line 1: _______________________
Address line 2: _______________________
City, ST, ZIP: _______________________
Best contact number (+ext): _______________________
We will contact you again in a few months for a second survey to see how things are going. For completing that survey, we will mail you another $50 thank-you check.
Last Screen:
Thank you for helping us with this important survey.
Please close your browser window to exit.
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APPENDIX B: POST-INSTALLATION SURVEY
PG&E Energy Management System (Post-Installation Survey)
General Business/Facility Info
1) Have your facility operating hours changed since installing the EMS system?
YES NO
IF YES:
a. What are the new operating hours?
Open 24/7 Open Close
Weekdays AM / PM AM / PM
Weekends/Holidays AM / PM AM / PM
2) Do any employees come in early or stay late?
YES NO
IF YES:
a. Outside of normal operating hours, when is the earliest an employee would come in? __________ AM / PM
b. Outside of normal operating hours, when is the latest an employee would leave? __________ AM / PM
3) Is any cleaning or maintenance work done outside of normal operating hours?
YES NO
IF YES:
a. When is the earliest someone would come in? __________ AM / PM
b. When is the latest someone would leave? __________ AM / PM
4) Have the number of employees and/or customers in your facility changed since the EMS was installed?
YES NO
IF YES:
a. How many people work at this facility? __________
b. How many customers visit this facility on weekdays? __________
c. How many customers visit this facility on weekends? __________
New1
w2g1a1 w2g1a2 w2g1a3
w2g1b1 w2g1b2 w2g1b3
w2g2
w2g2a
w2g2b
w2g3
w2g3a
w2g4
new4
w2g3b
w2g5a
w2g5b
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5) Who has access to the control system?
Building Management
Individual Tenants
Other – Specify: ________________________________________________________________________
________________________________________________________________________
6) How many people are able to adjust the lights, space temperatures, or other equipment?
__________
7) After installing the EMS, have you noticed any difference with your building’s energy usage and energy bills?
YES NO
Lighting Info
8) Other than the EMS installation, have any other changes been made to your lighting systems? (new or replaced fixtures, lamps with different wattages, installation of occupancy sensors, timers or photocells)
YES NO
IF YES:
a. What were these changes? _________________________________________________________________
_________________________________________________________________
9) How many different groups of lights does the EMS control in your facility?
__________
10) When are lights turned on & off by the EMS for each group of lights?
Repeat for number of groups in Q9
On Off
Group 1 AM / PM AM / PM
Group 2 AM / PM AM / PM
Group 3 AM / PM AM / PM
Group 4 AM / PM AM / PM
w2l1c
w2l1b
w2l1a
w2l1cs
new6
new7
new8
new8a
new9
new10a1 new10a2
new10e1
new10d1
new10c1
new10b1 new10b2
new10c2
new10d2
new10e2
new10h1
new10g1
new10f1 new10f2
new10g2
new10h2
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11) Are different light levels scheduled for different purposes or times of the day?
YES NO
IF YES:
a. Please describe… _________________________________________________________________
_________________________________________________________________
12) Do any employees have the ability to override or adjust the EMS lighting settings?
YES NO
13) What are the pros and cons of EMS’s control of your lighting system?
___________________________________________________________________________
___________________________________________________________________________
HVAC Info
14) Have any changes been made to your HVAC equipment since the EMS was installed?
YES NO
15) When does the EMS adjust space temperatures?
(Time of day, day of week, frequency of changes)
___________________________________________________________________________
___________________________________________________________________________
16) What are temperature settings in summer and winter, when the building is OCCUPIED?
Temp when Occupied Temp when Unoccupied
Summer
Winter
new11
new11a
new12
new13
new14
new15
w2h8a1 w2h8a2
w2h8b1 w2h8b2
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17) Are different temperature settings used for different purposes or different times of day?
YES NO
IF YES:
a. Please describe… _________________________________________________________________
_________________________________________________________________
18) Can the building occupants adjust these settings?
YES NO
IF YES:
a. Are there limits to the adjustments that can be made?
YES NO
19) What are the pros and cons with the EMS’s control of your space temperatures?
___________________________________________________________________________
___________________________________________________________________________
20) How often is your HVAC system serviced by an HVAC contractor?
___________________________________________________________________________
21) Does the EMS give you reminders about the need for servicing?
___________________________________________________________________________
Other Equipment
22) Besides lighting and HVAC systems, what other equipment in your building does the EMS monitor? (Cooking equipment, refrigeration, ventilation, office equipment, computers/servers, etc.)
Cooking equipment
Appliances
Refrigeration
Ventilation
Gas water heating
Electronic office or computer equipment
Electric water heating
Fork lift battery charger
Space heaters
w2h9
w2h9a
w2h6
w2h6a
new19
new20
new21
new22a new22b
new22c
new22d
new22e
new22f
new22g
new22h
new22i
new22j new22s
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Other: __________________________________________________________________
________________________________________________________________________
23) Have you changed the way this equipment is controlled as a result of the monitoring information you’re seeing?
YES NO
IF YES
a. What have you changed? _________________________________________________________________
_________________________________________________________________
24) Have you set up a control schedule for this equipment within the EMS?
YES NO
IF YES:
a. What is the schedule? _________________________________________________________________
_________________________________________________________________
Alarms & Notifications
25) Will the EMS send notices or alarms when systems aren’t operating as expected?
YES NO
IF YES:
a. What notices will it send? _________________________________________________________________
_________________________________________________________________
b. Have you received any notices/alarms?
YES NO
IF YES:
i. What were they, and what did you do in response to them?
_________________________________________________
_________________________________________________
_________________________________________________
_________________________________________________
new23
new23a
new24
new24a
new25
new25a
new25b
new25bs
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APPENDIX C: POST-INSTALLATION SURVEY RESPONSES
As part of the post-installation surveys, respondents were asked to list EMS pros and cons.
Their following are their comments, first for lighting and then for HVAC control:
Pros and Cons of EMS Control of your Lighting:
“There are no cons. The system in general is pretty foolproof. It takes everything
out of the hands of people, which has been a problem in the past.”
“Pro: Energy savings. Con: As of yesterday, we still had problems with it not working
properly.”
“Pro: Lights aren't left on 24/7 like they are in some stores. Can't think of a con.”
“Pro: Being able to set back during times the building is not occupied. Con: Not
being able to communicate at the beginning and after hours stuff. When you
energize the alarm, it shuts the lights off.”
“You can shut them down and turn them off in a perfect world, but that doesn't
necessarily work. If [vendor] spent more time with better installation, it would be
better for me.”
“Don't really have any negatives except the ability to schedule more events that way
I can program for a holiday without having to change my regular weekly schedule.
Just having greater control over the whole system is nice, being able to customize it
for the time of day… not having to adjust it on a regular basis for chasing the
sunset.”
“The pro is basically that we can more easily adjust the schedule. And beyond that,
there's really no downside.”
“Pro: Trimming a little bit of time instead of using the photocells when it's starting to
get a little dark and there's no benefit to having the lights on. Gaining efficiency
without loss of lighting. Con: Interior lighting is less efficient, comes on before
building is occupied. The [EMS] is still turning lights on and off on holidays.”
“Pro: Prevents our managers, employees or even customers from going and
adjusting the settings constantly to meet their individual wants. Cons: Something
wasn't set right and we had lights going off and a heater pumping heat so people
were burning up in the lobby. We couldn't get ahold of [vendor] to fix it remotely.
That was an issue initially.”
“Pro: It's enabling us to save energy, save costs. Cons: It's somewhat complex.”
Pros and Cons of EMS Control of your Space Temperatures:
“No cons, huge pros: One of the issues we had in the past before the installation of
the [EMS] was people playing with the thermostats and adjusting them up and down
and sometimes not adjusting them properly... Or guests in the front of the house in
one section was a little bit cold and would expect you to adjust the temperature even
though everyone else in the dining room was perfectly comfortable. It's really easy
now to tell the guests we don't have the ability to do that.”
“Employees don't have access. Now it's stable, as far as the temperatures go. I don't
have any cons at all.”
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“It will be great when my employees aren't able to get on and turn it down to 50
degrees and ice up my stuff and cost me thousands of dollars.”
“Pro: Cost, can lockout staff, the display, Con: Phone app, text file, support,
software, no explanation of features at installation.”
“Idea is good.”
“Pro: Having such specific control of the auditoriums and being able to stage when
they come on/off. Much more easier to use than the individual thermostats. No
drawbacks.”
“Pro: Can adjust temperatures to match business hours. Con: Default range was not
large enough.”
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APPENDIX D: GRAPHS OF HVAC ENERGY VS OUTSIDE AIR
TEMPERATURE
The figures on the following pages plot average measured HVAC energy use (amps) against
outside air temperature. Data is averaged into 2 °F bins. Each figure is for either the
occupied, unoccupied, or staff hours as defined in the report. Data is shown for the pre-
installation period (blue dots) and the post-installation period (orange dots). In addition,
solid lines indicate the model used to reflect the data, including any extrapolations. Note
that the HVAC energy use shown is not for all HVAC systems – at most sites a sample of
HVAC units was measured.
Each figure also includes the typical annual hours at each temperature bin (“Weather TMY3
Hours”), during the hours period. These hours are from local TMY3 data, and are presented
with the same scale on all figures, to allow comparisons.
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FIGURE D - 1A. KERN COUNTY RETAIL HVAC ENERGY VS OUTSIDE AIR
TEMPERATURE – OCCUPIED HOURS
FIGURE D – 1B. KERN COUNTY RETAIL HVAC ENERGY VS OUTSIDE AIR
TEMPERATURE – UNOCCUPIED HOURS
FIGURE D - 2A. ALAMEDA COUNTY RETAIL HVAC ENERGY VS OUTSIDE AIR
TEMPERATURE – OCCUPIED HOURS
FIGURE D – 2B. ALAMEDA COUNTY RETAIL HVAC ENERGY VS OUTSIDE AIR
TEMPERATURE – UNOCCUPIED HOURS
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FIGURE D – 3A. NEVADA COUNTY ASSEMBLY HVAC ENERGY VS OUTSIDE AIR
TEMPERATURE – OCCUPIED HOURS
FIGURE D – 3B. NEVADA COUNTY ASSEMBLY HVAC ENERGY VS OUTSIDE AIR
TEMPERATURE – UNOCCUPIED HOURS
FIGURE D – 4A. LAKE COUNTY ASSEMBLY HVAC ENERGY VS OUTSIDE AIR
TEMPERATURE – OCCUPIED HOURS
FIGURE D – 4B. LAKE COUNTY ASSEMBLY HVAC ENERGY VS OUTSIDE AIR
TEMPERATURE – UNOCCUPIED HOURS
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FIGURE D – 5A. STANISLAUS COUNTY ASSEMBLY/RESTAURANT HVAC
ENERGY VS OUTSIDE AIR TEMPERATURE – OCCUPIED HOURS
FIGURE D – 5B. STANISLAUS COUNTY ASSEMBLY/RESTAURANT HVAC
ENERGY VS OUTSIDE AIR TEMPERATURE – UNOCCUPIED HOURS
FIGURE D – 6A. SANTA CLARA COUNTY ASSEMBLY/RESTAURANT HVAC
ENERGY VS OUTSIDE AIR TEMPERATURE – OCCUPIED HOURS
FIGURE D – 6B. SANTA CLARA COUNTY ASSEMBLY/RESTAURANT HVAC
ENERGY VS OUTSIDE AIR TEMPERATURE – UNOCCUPIED HOURS
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FIGURE D – 7A. SANTA CLARA COUNTY SIT DOWN RESTAURANT HVAC
ENERGY VS OUTSIDE AIR TEMPERATURE – OCCUPIED HOURS
FIGURE D – 7B. SANTA CLARA COUNTY SIT DOWN RESTAURANT HVAC
ENERGY VS OUTSIDE AIR TEMPERATURE – STAFF HOURS
FIGURE D – 7C. SANTA CLARA COUNTY SIT DOWN RESTAURANT HVAC
ENERGY VS OUTSIDE AIR TEMPERATURE – UNOCCUPIED HOURS
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FIGURE D – 8A. SAN JOAQUIN COUNTY SIT DOWN RESTAURANT HVAC
ENERGY VS OUTSIDE AIR TEMPERATURE – OCCUPIED HOURS
FIGURE D – 8B. SAN JOAQUIN COUNTY SIT DOWN RESTAURANT HVAC
ENERGY VS OUTSIDE AIR TEMPERATURE – STAFF HOURS
FIGURE D – 8C. SAN JOAQUIN COUNTY SIT DOWN RESTAURANT HVAC
ENERGY VS OUTSIDE AIR TEMPERATURE – UNOCCUPIED HOURS
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FIGURE D – 9A. ALAMEDA COUNTY FAST FOOD RESTAURANT HVAC ENERGY
VS OUTSIDE AIR TEMPERATURE – OCCUPIED HOURS
FIGURE D – 9B. ALAMEDA COUNTY FAST FOOD RESTAURANT HVAC ENERGY
VS OUTSIDE AIR TEMPERATURE – UNOCCUPIED HOURS
FIGURE D – 10A. PLACER COUNTY FAST FOOD RESTAURANT HVAC ENERGY
VS OUTSIDE AIR TEMPERATURE – OCCUPIED HOURS
FIGURE D – 10B. PLACER COUNTY FAST FOOD RESTAURANT HVAC ENERGY
VS OUTSIDE AIR TEMPERATURE – UNOCCUPIED HOURS
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FIGURE D – 11A. MERCED COUNTY FAST FOOD RESTAURANT HVAC ENERGY
VS OUTSIDE AIR TEMPERATURE – OCCUPIED HOURS
FIGURE D – 11B. MERCED COUNTY FAST FOOD RESTAURANT HVAC ENERGY
VS OUTSIDE AIR TEMPERATURE – UNOCCUPIED HOURS
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APPENDIX E: ESTIMATES OF WHOLE BUILDING SAVINGS
USING CEUS BENCHMARK DATA We translated the percent of lighting and HVAC energy savings in this report into estimated
percents of total building energy savings, using benchmark data from the California
Commercial End Use Study (CEUS, accessed via the website
http://energyiq.lbl.gov/benchmark.jsp). This study of 2,790 commercial facilities collected
energy use information from numerous building types in all areas of California.
In order to determine what portion of electricity is typically used in buildings for lighting
and HVAC purposes, we collected CEUS information about the average electricity end-use
per square foot (kWh/sf, converted from kBTU/sf) for buildings of different type and
location. Table 4 (page 13) lists which types and locations were used, and how many
buildings were contained in the CEUS sample for each category. Note that this data included
all building vintages and sizes.
Table E - A below lists the resulting typical values from CEUS for Lighting, HVAC, Other, and
Total building electricity use in kWh per square foot.
TABLE E - A. CEUS ELECTRICITY USE PER FLOOR AREA USED TO NORMALIZE SAVINGS IN THE FACILITIES
Location Building Type
Lighting
kWh/sf
HVAC
kWh/sf
Other
kWh/sf
Total
kWh/sf
Kern County Retail 104 64 38 206
Alameda County Retail 140 48 66 253
Nevada County Assembly 34 26 40 100
Lake County Assembly 70 27 81 178
Stanislaus County Assembly / Restaurant 94 114 242 449
Santa Clara County Assembly / Restaurant 94 46 259 398
Santa Clara County Sit Down Restaurant 154 83 477 714
San Joaquin County Sit Down Restaurant 153 201 443 798
Alameda County Fast Food Restaurant 153 142 460 756
Placer County Fast Food Restaurant 155 165 363 682
Merced County Fast Food Restaurant 153 201 443 798
AVERAGE 118 101 265 485
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We multiplied the kWh/sf for lighting use by the annualized values of % savings we
estimated from our monitoring results, to get kWh/sf savings for lighting use. Similarly, we
multiplied the kWh/sf for HVAC use (sum of heating, ventilation, and cooling kWh/sf) by the
annualized values of % savings we estimated from our monitoring results, to get kWh/sf
savings for HVAC use.
Total building electricity use, as estimated using CEUS benchmarking data, went down with
the use of the EMS in all 11 facilities. Savings ranged from 1% to 17%, as the effects on
HVAC energy use outweighed the effects of lighting use. We estimated that the lighting and
HVAC savings from the eleven buildings translated into an average building savings of 6%,
or 25 kWh per square foot. Note, we did not validate these estimates using a whole building
approach.
TABLE E - B. TOTAL BUILDING ELECTRICITY SAVINGS ESTIMATES BASED ON TYPICAL CEUS DATA
LocationCalifornia
Climate ZoneBuilding Type Lighting %
Savings
HVAC %
Savings
Building %
Savings
Building
kWh/sf
Savings
Kern County 13 Retail 14% 6% 9% 18
Alameda
County12 Retail 0% 20% 4% 9
Nevada
County11 Assembly - 25% 6% 6
Lake County 2 Assembly 36% 9% 15% 28
Stanislaus
County12
Assembly /
Restaurant7% - 1% 7
Santa Clara
County4
Assembly /
Restaurant-4% 31% 3% 10
Santa Clara
County4
Sit Down
Restaurant-6% 19% 1% 6
San Joaquin
County12
Sit Down
Restaurant8% 28% 9% 68
Alameda
County12
Fast Food
Restaurant-5% 18% 2% 18
Placer County 11Fast Food
Restaurant17% - 4% 26
Merced
County12
Fast Food
Restaurant1% 11% 3% 22
AVERAGE 6% 18% 5% 20