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IMPROVING FINANCIAL ANALYTICS AND FORECASTING WITH...
Transcript of IMPROVING FINANCIAL ANALYTICS AND FORECASTING WITH...
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
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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
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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
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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
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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
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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