VELCO Long-Term Demand Forecast Methodology Overview

1. VELCO Long-Term Demand ForecastMethodology Overview Eric Fox Oleg Moskatov Itron, Inc. April 17, 2008

2. Overview • Methodology • SAE energy model • Hourly Load and Peak Demand Model • Assumptions • Weather data • Normal weather • Economic drivers • End-use saturation and efficiency trends • Price • Preliminary Results • Recent peak and energy trends • Hourly load build-up results • Peak and energy forecast

3. Forecasting Approaches • Three general approaches are used for forecasting long-term peak demand: • Load factor model • Load factor = Average Demand / Peak Demand • Peak Forecast = Energy Forecast / Hours * Load Factor • Generalized econometric model • Peak Forecast = ƒ(peak-day weather, customers, economic activity) • Build-up approach • Combine class energy forecasts with hourly profiles • Aggregate to system load • Find system peak Load factors and econometric models are adequate for short-term forecasting, but can’t capture the impact of changing load diversity on long-term peak demand.

4. Build-up Forecast Approach • Develop long-term energy forecasts by customer class • Residential, Commercial, Industrial, and Other • Combine class energy forecasts with class hourly load profile models • Evaluate using end-use hourly load and energy estimates • Aggregate class profiles to generate a long-term system forecast and extract the monthly system peak demand • Calibrate to weather-normalized 2008 demand estimates

5. System Peak and Energy

6. Summer Peak

7. System Peak Demand AnalysisDaily Peak Demand (MW) 2002 to 2007 Significantly less temperature- sensitive load than compared with other regions

8. Winter and Summer Monthly Peaks (MW) But not surprisingly, peaks are driven by heating and cooling demand

9. System Peak Demand (Weekdays vs. Weekends) Summer peak demand always falls during the week capturing both commercial and residential cooling loads Winter peaks also tend to fall during the week-days, but winter week-end peaks can be nearly as high on cold days

10. Monthly peak demand (MW) Summer peak: 10 MW per year Winter peak: 6 MW per year Since 2002, peak demand has been increasing roughly 1.0% per year

11. System Monthly Load Factor Load Factor Moving Average Trend The load factor appears to be trending down slightly implying peak demand is growing slightly faster than energy

12. Peak-Day System Hourly Load Profile (MW) System Residential Commercial Industrial Small differences in customer class load growth can have a significant impact on the peak and its timing

13. Peak-Day Residential Load Profile (MW) Residential Base Use Cooling Changes in end-use sales growth in turn impact customer class hourly load

14. Hourly And Peak Forecast Build-Up Model Combine energy forecast and hourly class profiles using MetrixLT Need class and end-use energy and profile forecasts Monthly/Annual Energy Forecast Long-Run Load Shape Forecasting System Class or End Use Profiles Hourly System Forecast

15. Long-Term Energy Forecasting • Model that can account for economic changes as well as long term structural changes • Economic impacts – income, household size, household growth • Price impacts • Structural changes – saturation, efficiency, floor space, and thermal integrity trends • Weather impacts • Appropriate interaction of these variables • Approaches • End-Use Modeling Framework – REEPS and COMMEND • Statistically Adjusted End-Use Model – Econometric model specification

16. SAE Modeling Approach • Blend end-use concepts into an econometric modeling framework: • Average Use = Heating + Cooling + Other Use • Define components in terms of its end use structure: • Cooling = f (Saturation, Efficiency, Utilization) • Utilization = g (Weather, Price, Income, Household Size) • Leverage off of EIA census region end-use forecasts • Adjust for known differences in service area saturations

17. Residential & Commercial SAE Model Regions

18. Statistically Adjusted End-use Modeling (cont.) Estimate model using Ordinary Least Squares:

19. Residential Cooling End Use

20. Residential Cooling Saturation Trends

21. Residential Cooling Efficiency Trends • Efficiency for cooling equipment is given for the total US • Seasonal Energy Efficiency Ratio (SEER) is defined as a ratio of the total cooling of a central unitary air conditioner or a unitary heat pump in Btu during its normal annual usage period for cooling and the total electric energy input in watt-hours during the same period

22. Residential Cooling Index (Annual kWh)

23. Residential XCool Variable Monthly cooling requirements (kWh) Average cooling use increases with increasing air conditioning saturation

24. Residential XHeat Variable Monthly heating requirement (kWh) Average heating use declines with declining heating saturation

25. Residential Non HVAC End-uses

26. Residential Non HVAC End-uses (cont.)

27. Residential XOther Variable Monthly base use requirement (kWh)

28. Impact of 2007 Energy Act - Lighting New England Lighting UEC (2007-2008) • 2007 Energy Independence and Security Act introduces a number of new appliance efficiency standards • New lighting standards have the most significant impact on residential load • Lighting accounts for approximately 20% of residential “other” use Current lighting standards New lighting standards New England XOther Sharp drop in electric sales results Results in a sharp drop in base use

29. New England Residential Forecast Comparison (GWh) Due to the high lighting replacement rate, residential electric sales drop off quickly once the new standards go in place. Residential energy use is 2.5% lower by 2013

30. Estimated SAE Model – Residential Average Use

31. Predicted Vs. Actual Average Use

32. Residential Sales Forecast by End-Use (GWh) Heating Cooling Base Use

33. Residential End-Uses (EIA) • Heating – electric resistance, heat pump • Cooling – CAC, room air conditioning, heat pump • Other Use • Water heating • Cooking • Refrigeration • Second refrigerator • Freezer • Dishwasher • Clothes washer • Dryer • Microwave • Color TV • Lighting • Miscellaneous

34. Commercial End-Uses (EIA) • Heating • Cooling • Other Use • Ventilation • Water heating • Cooking • Refrigeration • Lighting • Miscellaneous

35. Vermont Monthly Sales Forecast Models • Customer Classes • Residential • Commercial • Industrial • Other • Monthly residential and commercial class models are estimated using an SAE specification • The industrial sales model estimated using a generalized econometric model • We assume historical DSM activity is embedded in the sales data and thus in the estimated models

36. Data Sources • Monthly Sales, Customer and Revenue Data • Energy Information Agency • January 1999 to November 2007 • Depending on system loss factor, sales data account for 95% to 97% of delivered system energy • Weather Data • Historical daily maximum and minimum temperatures • Burlington Airport, 1970 to current • Evaluated other weather stations, however, there were too many holes in the data series • Burlington-based HDD and CDD explain state-level sales well. • Price Data • Price series was calculated from reported revenues, sales, and Vermont CPI • Price calculated as a 12-month moving average of the prior twelve-month average rate (real basis) • Assume constant real price in the forecast

37. Data Sources • Economic Data • Fall 2007 Vermont economic forecast by Economy.com • Population, number of households, real personal income • Gross State Product, manufacturing output, non-manufacturing and manufacturing employment • Final forecast will be based on Economy.com’s current state economic forecast • End-Use Saturation and Efficiency Trends • Developed from the 2007 EIA Energy Outlook Forecast for New England • Currently updating efficiency projections to reflect the recently passed energy bill • End-use saturation trends will be calibrated against recent state and Burlington Electric residential saturation surveys

38. Long-Term Vermont Economic Projections

39. Long-Term US Economic Projections

40. Preliminary Class Energy Forecasts (MWh) Residential 1.1% Commercial 1.1% Industrial 0.2% Other No Growth

41. Preliminary Class Energy (MWh)

42. Class Hourly Profile Data Sources • Load Data • Burlington Electric Load Research Data • Residential • Small General Service • Large General Service • Other Load Research Data • Industrial • Street Lighting • Daily maximum and minimum temperatures • Burlington Airport • Daily calendar • Day of the week, month, holiday, hours of sunlight

43. Class Hourly Profile Models • Class Hourly Model Structures • Twenty-four hourly regression models • HDD and CDD • Month, Day of the Week, Holidays • Hours of Light • Estimation Period • January 1, 2004 to December 31, 2006

44. Calculation of Daily Normal Weather • Ten years daily average temperature for Burlington • 1998-2007 • Rank and Average approach • Daily average temperatures ranked from highest to lowest and within each year then averaged across all 10 years • Map daily normal ranked weather data to a typical daily weather pattern • Typical year weather pattern calculated from historical daily weather data • Map the ranked temperature data to the typical year weather pattern • Used MetrixLT for calculating daily normal temperature series

45. Chaotic Daily Normal Weather Series • Daily normal weather series mapped to the average ten-year weather pattern

46. Residential Load Model (kW per customer)

47. Large General Service Load Model

48. Residential End-Use Profiles • Cooling, heating, and other use profiles estimated from end-use weather response models • Data is based on building simulation runs • Models simulated for 2004 to 2007 using Burlington actual weather • End-use profiles scaled to residential profile model

49. Residential Cooling Profile

50. Residential Heating Profile