OVERVIEW OF LOAD FORECASTING METHODOLOGY Northeast Utilities Economic & Load Forecasting Dept. May 1, 2008 UConn/NU Operations Management.
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OVERVIEW OF LOAD FORECASTING
METHODOLOGYNortheast UtilitiesNortheast Utilities
Economic & Load Forecasting Dept.Economic & Load Forecasting Dept.
May 1, 2008May 1, 2008UConn/NU Operations ManagementUConn/NU Operations Management
Forecast PurposeForecast Purpose
Provide an Provide an indicationindication of expected sales volumes 1 to of expected sales volumes 1 to 5 years out, given certain assumptions5 years out, given certain assumptions Considered “most likely” with equal chance of too high or too Considered “most likely” with equal chance of too high or too
lowlow Therefore, Company should plan for a range of possible Therefore, Company should plan for a range of possible
outcomesoutcomes
General guidance regarding sales trends, based on General guidance regarding sales trends, based on economic theory and available dataeconomic theory and available data
Used primarily for financial forecasting & rate casesUsed primarily for financial forecasting & rate cases Some use for transmission and supply planningSome use for transmission and supply planning
Forecast TheoryForecast Theory
Economic theory drives the forecasting structure and Economic theory drives the forecasting structure and modelsmodels
Consumption is a function of a primary economic Consumption is a function of a primary economic driver, price of the product, price of competing driver, price of the product, price of competing products, and a vector of other relevant variablesproducts, and a vector of other relevant variables
Theoretical structure is critical to withstand scrutiny of Theoretical structure is critical to withstand scrutiny of forecast reviewforecast review Particularly important in rate case processParticularly important in rate case process Becoming more important within corporationBecoming more important within corporation
Forecast PracticeForecast Practice
Good forecast must have good inputsGood forecast must have good inputs Accurate billed vs. calendar sales, and timely billing and Accurate billed vs. calendar sales, and timely billing and
booking of salesbooking of sales Accurate customer countsAccurate customer counts Accurate and relevant economic dataAccurate and relevant economic data
Forecasts are dynamic and will vary with each Forecasts are dynamic and will vary with each forecast solutionforecast solution Updated historical data (internal and external)Updated historical data (internal and external) Relationship of sales to their drivers (elasticities) updatedRelationship of sales to their drivers (elasticities) updated New forecasts for economic, price, C&LM, ED, customer New forecasts for economic, price, C&LM, ED, customer
specific, etc.specific, etc. Model differences and changes in customer behaviorModel differences and changes in customer behavior
Forecast MethodologiesForecast Methodologies
Combine the strengths of multiple methodologies:Combine the strengths of multiple methodologies:
Primarily “End-Use” Models Primarily “End-Use” Models Sales = #Customers * Use per CustomerSales = #Customers * Use per Customer
Within various customer classes and end-useWithin various customer classes and end-use Enables capturing of structural changes in demandEnables capturing of structural changes in demand However, extremely data intensiveHowever, extremely data intensive
Blended with Econometric Models (“causal linear regression”)Blended with Econometric Models (“causal linear regression”) y = a + bxy = a + bx1 1 + cx+ cx22 + … + …
Far less data intensive (time series of y and x’s)Far less data intensive (time series of y and x’s) But, assumes historic relationships will continueBut, assumes historic relationships will continue
Supplemented by JudgmentSupplemented by Judgment AE input on customer specific changesAE input on customer specific changes C&LM and ED impactsC&LM and ED impacts Analyst judgmentAnalyst judgment
Industrial (net of Special Contracts)
Where to Begin?Where to Begin?
Annualized Industrial Gas SalesAnnualized Industrial Gas Sales
PSNH Normalized Residential Calendar Sales 12 Month Ending
2600
2700
2800
2900
3000
3100
3200
3300
Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08
GWH
Normalized Actual 2008 Budget
We’d Like to Begin Here!We’d Like to Begin Here!
END-USE MODELSales = # of Units * Use per Unit
GENERIC MODEL EXAMPLE - RESIDENTIAL
Forecast Total Market Residential Customers = 1,000,000
Forecast End-Use Saturations - % of Total Market with End Use
25% of Residential Customers Have Electric Water Heaters
Forecast Usage per End Use
Electric Water Heater Usage = 4,000 KWH/Year
Sales = Total Market * Saturation * Usage
Sales = 1,000,000 * 25% * 4000 KWH = 1000 GWH
Customer ForecastCustomer Forecast
y = #Customersy = #CustomersXX11 = Housing Starts = Housing Starts
XX22 = Own Price = Own Price
XX33 = Competing Price = Competing Price
XX44 = lag(#Customers) = lag(#Customers)
y = a + bxy = a + bx11 + cx + cx22 + dx + dx33 + ex + ex44
Need both historic and forecasted time series for y and each xNeed both historic and forecasted time series for y and each x Regression run over historic time period (say 1990 – 2007)Regression run over historic time period (say 1990 – 2007) Solve equation over forecast time period (say 2008 - 2013)Solve equation over forecast time period (say 2008 - 2013)
Graphing the Data Always Helps!!!Graphing the Data Always Helps!!!
Residential Customers and Connecticut Permits
0.0%
1.0%
2.0%
3.0%
Perc
en
t C
han
ge in
Resid
en
tial C
usto
mers
0
5,000
10,000
15,000
20,000
25,000
30,000
CT
Perm
its
Residential Customers CT Permits
End-Use Saturation ForecastEnd-Use Saturation Forecast
Energy Information Administration (EIA) Energy Information Administration (EIA) historic regional datahistoric regional data
Adjusted based on Company-specific Adjusted based on Company-specific Customer SurveysCustomer Surveys
Trend model applied to create forecasted Trend model applied to create forecasted saturationssaturations
Usage ForecastUsage Forecast
Usage is adjusted by a Price Elasticity estimate:Usage is adjusted by a Price Elasticity estimate:
y = Use per Customery = Use per CustomerXX11 = Own Price = Own Price
XX22 = Income = Income
XX33 = Competing Price = Competing Price
XX44 = lag(#Use per Customer) = lag(#Use per Customer)
y = a + bxy = a + bx11 + cx + cx22 + dx + dx33 + ex + ex44
estimate of “b” is used to develop price elasticityestimate of “b” is used to develop price elasticity Base Usage is adjusted throughout the forecast based Base Usage is adjusted throughout the forecast based on forecast of change in price times elasticity estimateon forecast of change in price times elasticity estimate
End-Use EquationEnd-Use Equation
Sales =Sales =(Customers * Saturation EU1) * (Base (Customers * Saturation EU1) * (Base Usage EU1 * Price Elasticity Impact) + Usage EU1 * Price Elasticity Impact) +
(Customers * Saturation EU2) * (Base Usage (Customers * Saturation EU2) * (Base Usage EU2 * Price Elasticity Impact) + ……..EU2 * Price Elasticity Impact) + ……..
Across multiple end-uses for each of Residential, Commercial and Across multiple end-uses for each of Residential, Commercial and IndustrialIndustrial
Economic DriversEconomic Drivers
• EmploymentEmployment• Personal IncomePersonal Income• Housing Starts or StockHousing Starts or Stock• Gross State ProductGross State Product• Manufacturing Worker HoursManufacturing Worker Hours• Industrial ProductionIndustrial Production• InflationInflation
Other Forecast InputsOther Forecast Inputs
• Appliance Efficiency Standards Appliance Efficiency Standards • Company-sponsored Programs (DSM, Company-sponsored Programs (DSM,
Economic Development)Economic Development)• Weather (forecast assumes “normal” Weather (forecast assumes “normal”
weather)weather)• Vulnerable LoadVulnerable Load• Self-generationSelf-generation• Large Customer SurveysLarge Customer Surveys
How to Assess Forecast ResultsHow to Assess Forecast Results
• Degree of confidence in quality of data inputsDegree of confidence in quality of data inputs• Degree of confidence in model diagnostics (e.g., Degree of confidence in model diagnostics (e.g.,
regression stats)regression stats)• Changes in annualized sales to show trends in Changes in annualized sales to show trends in
growthgrowth• Assess YTD growth in salesAssess YTD growth in sales• Look for patterns in Large Customer’s usageLook for patterns in Large Customer’s usage• Residential customers and Use per Customer trendsResidential customers and Use per Customer trends• Economic AssessmentEconomic Assessment• Monitor industry trends, technology trends, efficiency Monitor industry trends, technology trends, efficiency
trendstrends• Look at ISO-NE Load Forecast Comm. survey results Look at ISO-NE Load Forecast Comm. survey results
to compare against other regional trendsto compare against other regional trends
Risks to the ForecastRisks to the Forecast
There are many. The forecast will be wrong!There are many. The forecast will be wrong!
• WeatherWeather• EconomicsEconomics• PricePrice• Data QualityData Quality• Model ErrorModel Error• Unknown and Unquantifiable – “The future doesn’t always Unknown and Unquantifiable – “The future doesn’t always
happen the way we said it would.”happen the way we said it would.”
Summary/ConclusionsSummary/Conclusions
• Load Forecasting at NU employs many of the Load Forecasting at NU employs many of the forecasting techniques covered in this classforecasting techniques covered in this class
• Each model methodology has its strengthsEach model methodology has its strengths• All are data intensive (some more than others)All are data intensive (some more than others)• Load Forecasting is not a precise scienceLoad Forecasting is not a precise science• Experience and judgment are criticalExperience and judgment are critical• The forecast will be wrongThe forecast will be wrong• Need to develop plans to manage the imprecisionNeed to develop plans to manage the imprecision
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