IMPROVING FINANCIAL

ANALYTICS AND

FORECASTING WITH AMI DATA

17TH ANNUAL ENERGY

FORECASTING MEETING / EFG

BOSTON, MA

APRIL 3-5, 2019

OVERVIEW

» AMI data support a range of improvements in financial analysis and forecasting.

» AMI data support a paradigm shift in forecasting

» Billing data will become more accurate

» Calendar month sales can be measured directly

• Actual calendar month sales can then be modeled

» Daily sales can be measured directly

• Increased leverage and power of daily data

• Logic Flow: Daily → Cycles → Sales and unbilled

» Daily tracking can be implemented

2

WHERE THE DATA COMES FROM

» A blurt is a message, usually generated about every second

» It contains register data and instantaneous data

» The frequency at which the messages are uploaded and stored is programmed into the meter, e.g., every 15 minutes

3

WHAT DATA ARE AVAILABLE

» We are mostly interested in the VA-hour and Watt-hour registers

» Data are retrieved by the data collection system

» Data are uploaded into Meter Data Management system

» Register values are stored and used to compute interval data volumes

• KWh = (WH Register (t) – WH Register (t-1)) / 1000

• KVAh = (VAH Register(t) – VAH Register (t-1))/1000

» Register reads can be used to compute:

• Total sales (KWh) for a billing cycle

• Total sales for a day

• Total sales for a calendar month

• Unbilled sales

• Customer max “demand” in KWh or KVAh

• KWh delivered and received

4

AMI DATA AT THE CLASS LEVEL

USE CASES FOR AGGREGATED AMI DATA

FORECASTING AND FINANCIAL GEOMETRY» A = Delivered in prior month(s), billed in current month = prior month unbilled

» B = Delivered in current month, billed in current month

» C = Delivered in current month, unbilled at end of month

7

AMI Interval Data

June Billing Cycles

Aug Billing Cycles

July

Unbilled

Corner

A

July Billing Cycles

B

C

WHAT CHANGES WITH AMI DATA

» Billing cycles are still laid out on a monthly calendar• Cycles are used to balance billing and call center loads

• Billing systems call MDM to compute billing determinants

• Cycle start and stop dates are used for this calculation

» With AMI, billing determinants become cleaner• Reads are continuous – no longer route based

• Cycle dates are used for calculations.

• Usage = difference between register reads at specific date/times

» Standard monthly approach still works• Sales in UPD (use per day) or UPC_PD (use per customer per day)

• Compute cycle HDD and CDD per billing day

• Monthly UPC_PD = F(Econ, Price, HDD_PD, CDD_PD)

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CURRENT METHOD: BILLED SALES MODEL

» Estimate billing cycle models• Cycle weighted HDD_PD, CDD_PD

• UPC_PD = F(..., HDD_PD, CDD_PD)

• Sales = UPC_PD * Cust * BillingDays

• Weather adjust (Normal cycle weighted)

» Simulate calendar month• Calendar month HDD_PD, CDD_PD

• CalSales = UPC_PD * Cust * CalDays

• Weather adjust (Normal Cal HDD, CDD)

» Simulate unbilled corner• Unbilled corner HDD_PD, CDD_PD

• UnbilledSales = UPC_PD * Cust * UnbilledDays

Simulate

Calendar

Month

Revenue

Month

Model

Simulate

Unbilled

Sales

OPTION A: CALENDAR MONTH MODELING

» Estimate calendar month models• Calendar month HDD_PD, CDD_PD

• UPC_PD = F(..., HDD_PD, CDD_PD)

• CalSales = UPC_PD * Cust * CalDays

• Weather adjust (Normal HDD_PD, CDD_PD)

» Simulate billing month• Cycle weighted HDD_PD, CDD_PD

• Cycle Sales = UPC_PD * Cust * BillingDays

• Weather adjust with Normal Cal HDD, CDD

» Simulate unbilled corner• Unbilled corner HDD_PD, CDD_PD

• UnbilledSales = UPC_PD * Cust * UnbilledDays

Calendar

Month

Model

Simulate

Revenue

Month

Simulate

Unbilled

Sales

AMI CALENDAR MONTH DATA

Residential

Use Per Customer Per Day

HOW MUCH MONTHLY DATA DO YOU NEED

» One year of AMI data is enough to get started.

» To get a longer history, stack the data. For example:• Use billing month data from 2000 to 2015

- Monthly use per customer per billing day

- Monthly HDD, CDD per billing day

• Use AMI calendar data from 2017 on

- Monthly use per customer per calendar day

- Monthly HDD, CDD per calendar day

» Stack the data and estimate one model

» AMI data provides actual calendar month sales. • You may as well use it to estimate models.

• It also clears up month-end confusion if you can get actual values in time.

OPTION B: DAILY MODELING

» Estimate daily sales models• Daily HDD, CDD

• UPC = F(..., HDD, CDD)

• Daily Sales = UPC_PD * Cust

• Weather adjust (Normal HDD, CDD)

• Sum to get calendar months totals

- Actual sales

- Weather sales

» Use billing cycles to calculate• Rev month sales (compare with billing data)

• Rev month weather adjustment

» Use unbilled cycles to calculate• Unbilled energy

Daily

Model

Results

Cycle

Calculations

Revenue

Month

Results

Unbilled

Sales

MODELING WITH DAILY DATA

Residential

Daily Use Per Customer

DAILY DATA IS POWERFUL

» Monthly AMI data with monthly weather is clean

» But daily data has more leverage –> more modeling power• Especially on the cold side (in this case)

» Daily data provides better basis for weather adjustments and variance analysis calculations (multi-part splines, etc.)

Monthly Data Daily Data

EXAMPLE OF DAILY MODEL -- RES UPC

EXAMPLE OF DAILY MODEL -- RES UPC

SIMULATIONS WITH NORMAL WEATHER

DAILY NORMAL WEATHER OPTIONS» Rank and Average

» Average by Date

CDD

HDD

Avg DryBulb

Avg DryBulb10 year

15 year

20 year

Smooth

CDD

HDD

ORDERING FOR RANK AND AVERAGE» 2018 vs Normal Rank and Average

» Ordered by Actual 2018 Pattern

Actual 2018

Rank and Average

Actual 2018

Ordered Rank and Average

WEATHER ADJUSTMENTS -- RANK & AVERAGE

Actual Daily Sales (GWh)

Adjusted Daily Sales (GWh)

Weather Sales

WEATHER ADJUSTMENTS -- SMOOTH NORMAL

Actual Daily Sales (GWh)

Adjusted Daily Sales (GWh)

Weather Sales

METHOD COMPARISON

Actual

Normal

Weather Sales

Smooth Normal

Rank and Avg Normal

EXAMPLE OF CYCLE CALCS

» Run daily results through cycles:• Revenue month sales

• Revenue month weather sales

• Unbilled salesActual Rev Month Sales

Sales from Daily AMI

Rev Month

Weather Sales

Unbilled

Sales

Normal

Sales

Daily Results

Revenue

Month

Results

DAILY MODELING ENABLES DAILY TRACKING

» Daily revenue tracking steps • Use daily model to develop a daily energy budget

• Use AMI data to calculate actual daily energy sales

• Use daily model to forecast to end of month.

• Use daily model to weather adjust and to track against budget

25

Commercial Class Daily Budget (GWh)

June

Daily Budget

AMI ActualForecast

FORECASTING PARADIGM SHIFT

» This is a paradigm shift ranking with• Growth rate modeling (pre 1970’s)

• Econometric modeling and forecasting (early 1970’s)

• End-use forecasting (1975)

• SAE forecasting (1996)

• AMI monthly and daily forecasting (2016+)

» It requires an aggregated AMI data flow• Based on financial connectivity

• Timeliness is a challenge

• The promise is:

- More modeling power

- Better accuracy and improved clarity

- Improved visibility (sooner and better)

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SUMMARY

» Billing data are complex and can be confusing• Confusion leads to lack of clarity and modeling error

» AMI data are simple and clear

• Data are more granular

• Data are more timely

» Financial analysis can be improved with AMI data

• Improved clarity – how strong is the business

• Improved visibility – where are we headed

• Improved accuracy – smaller variances

• Improved confidence – foundation for better decisions

» And new processes can be implemented

» In our experience, the Executive Team expects these types of improvements and benefits from the AMI investment