OVERVIEW OF LOAD FORECASTING METHODOLOGY Northeast Utilities Economic & Load Forecasting Dept. May 1, 2008 UConn/NU Operations Management

Forecast Purpose  Provide an indication of expected sales volumes 1 to 5 years out, given certain assumptions  Considered “most likely” with equal chance of too high or too low  Therefore, Company should plan for a range of possible outcomes  General guidance regarding sales trends, based on economic theory and available data  Used primarily for financial forecasting & rate cases  Some use for transmission and supply planning

Forecast Theory  Economic theory drives the forecasting structure and models  Consumption is a function of a primary economic driver, price of the product, price of competing products, and a vector of other relevant variables  Theoretical structure is critical to withstand scrutiny of forecast review  Particularly important in rate case process  Becoming more important within corporation

Forecast Practice  Good forecast must have good inputs  Accurate billed vs. calendar sales, and timely billing and booking of sales  Accurate customer counts  Accurate and relevant economic data  Forecasts are dynamic and will vary with each forecast solution  Updated historical data (internal and external)  Relationship of sales to their drivers (elasticities) updated  New forecasts for economic, price, C&LM, ED, customer specific, etc.  Model differences and changes in customer behavior

Forecast Methodologies Combine the strengths of multiple methodologies:  Primarily “End-Use” Models  Sales = #Customers * Use per Customer  Within various customer classes and end-use  Enables capturing of structural changes in demand  However, extremely data intensive  Blended with Econometric Models (“causal linear regression”)  y = a + bx 1 + cx 2 + …  Far less data intensive (time series of y and x’s)  But, assumes historic relationships will continue  Supplemented by Judgment  AE input on customer specific changes  C&LM and ED impacts  Analyst judgment

Industrial (net of Special Contracts) Where to Begin? Annualized Industrial Gas Sales

We’d Like to Begin Here!

END-USE MODEL Sales = # of Units * Use per Unit

Customer Forecast y = #Customers X 1 = Housing Starts X 2 = Own Price X 3 = Competing Price X 4 = lag(#Customers) y = a + bx 1 + cx 2 + dx 3 + ex 4  Need both historic and forecasted time series for y and each x  Regression run over historic time period (say 1990 – 2007)  Solve equation over forecast time period (say )

Graphing the Data Always Helps!!!

End-Use Saturation Forecast  Energy Information Administration (EIA) historic regional data  Adjusted based on Company-specific Customer Surveys  Trend model applied to create forecasted saturations

Usage Forecast Usage is adjusted by a Price Elasticity estimate: y = Use per Customer X 1 = Own Price X 2 = Income X 3 = Competing Price X 4 = lag(#Use per Customer) y = a + bx 1 + cx 2 + dx 3 + ex 4  estimate of “b” is used to develop price elasticity  Base Usage is adjusted throughout the forecast based on forecast of change in price times elasticity estimate

End-Use Equation Sales = (Customers * Saturation EU1) * (Base Usage EU1 * Price Elasticity Impact) + (Customers * Saturation EU2) * (Base Usage EU2 * Price Elasticity Impact) + …….. Across multiple end-uses for each of Residential, Commercial and Industrial

Economic Drivers Employment Employment Personal Income Personal Income Housing Starts or Stock Housing Starts or Stock Gross State Product Gross State Product Manufacturing Worker Hours Manufacturing Worker Hours Industrial Production Industrial Production Inflation Inflation

Other Forecast Inputs Appliance Efficiency Standards Appliance Efficiency Standards Company-sponsored Programs (DSM, Economic Development) Company-sponsored Programs (DSM, Economic Development) Weather (forecast assumes “normal” weather) Weather (forecast assumes “normal” weather) Vulnerable Load Vulnerable Load Self-generation Self-generation Large Customer Surveys Large Customer Surveys

How to Assess Forecast Results Degree of confidence in quality of data inputs Degree of confidence in quality of data inputs Degree of confidence in model diagnostics (e.g., regression stats) Degree of confidence in model diagnostics (e.g., regression stats) Changes in annualized sales to show trends in growth Changes in annualized sales to show trends in growth Assess YTD growth in sales Assess YTD growth in sales Look for patterns in Large Customer’s usage Look for patterns in Large Customer’s usage Residential customers and Use per Customer trends Residential customers and Use per Customer trends Economic Assessment Economic Assessment Monitor industry trends, technology trends, efficiency trends Monitor industry trends, technology trends, efficiency trends Look at ISO-NE Load Forecast Comm. survey results to compare against other regional trends Look at ISO-NE Load Forecast Comm. survey results to compare against other regional trends

Risks to the Forecast There are many. The forecast will be wrong! Weather Weather Economics Economics Price Price Data Quality Data Quality Model Error Model Error Unknown and Unquantifiable – “The future doesn’t always happen the way we said it would.” Unknown and Unquantifiable – “The future doesn’t always happen the way we said it would.”

Summary/Conclusions Load Forecasting at NU employs many of the forecasting techniques covered in this class Load Forecasting at NU employs many of the forecasting techniques covered in this class Each model methodology has its strengths Each 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 science Load Forecasting is not a precise science Experience and judgment are critical Experience and judgment are critical The forecast will be wrong The forecast will be wrong Need to develop plans to manage the imprecision Need to develop plans to manage the imprecision