Accounting for Price in Your Forecast - Measures and Methodologies

Transcription

1 Electric / Gas / Water Accounting for Price in Your Forecast - Measures and Methodologies Itron s Forecasting Brown Bag Seminar March 21, 2006 Frank A. Monforte, Ph.D. J. Stuart McMenamin, Ph.D.

2 Please Remember In order to help this session run smoothly, your phones are muted. If you need technical assistance during the meeting, dial *0 and you will be connected to a Premiere Conferencing technician. If you need to give other feedback to the presenter during the meeting, such as, slow down or need to get the presenters attention for some other reason, use the pull down menu in the seating chart and we will address it right away. If you have general questions regarding the presentation, please type your question in the Q&A box in the bottom, right corner. We will try to answer as many questions as we can at the end of the session. If you would like to make the presentation portion of the screen larger, click on the expand button on the toolbar.

3 Themes for 2006 Brown Bags Monthly sales forecasting Adding end-use structure Accounting for Price Methods for computing unbilled sales Short-term system operations and retail forecasting Latest thinking on model structures Clustering customer loads

4 Average Residential Natural Gas Prices 14 Avg Price Real Avg Price $ $/MCF

5 Change in Emphasis on Price Impacts Gas Perspective About the Same 15% Somewhat Important 27% High Priority 58%

6 Average Residential Electricity Prices 20 Average Price Real Average Price $

7 Change in Emphasis on Price Impacts Electric Perspective About the Same 35% High Priority 32% 68% Somewhat Important 33%

8 Price Response from an End-Use Perspective Five types of economic decisions End-use acquisition decisions, which determine equipment saturation levels Fuel choice decisions in new construction, replacement and conversions (e.g. space heating, cooking, water heating) End-use efficiency decisions at the time of equipment purchase Measure and device decisions that impact efficiency and usage (e.g. set-back thermostats and occupancy sensors) Utilization levels (e.g. turning lights off, thermostat settings)

9 Short-Run Price Response Five types of economic decisions End-use acquisition decisions, which determine equipment saturation levels Fuel choice decisions in new construction, replacement and conversions (e.g. space heating, cooking, water heating) End-use efficiency decisions at the time of equipment purchase Measure and device decisions that impact efficiency and usage (e.g. set-back thermostats and occupancy sensors) Utilization levels (e.g. turning lights off, thermostat settings)

10 Long-Run Price Response Five types of economic decisions End-use acquisition decisions, which determine equipment saturation levels Fuel choice decisions in new construction, replacement and conversions (e.g. space heating, cooking, water heating) End-use efficiency decisions at the time of equipment purchase Measure and device decisions that impact efficiency and usage (e.g. set-back thermostats and occupancy sensors) Utilization levels (e.g. turning lights off, thermostat settings)

11 Measuring Price Sensitivity ( P,XOther) e Y = F + PriceResponse = dy dp Problem with this is the derivative is sensitive to the units of measure, e.g. MWh/$, Therm/$

12 General Definition of Price Elasticity ( P,XOther) e Y = F + Elast = dy dp P Y = df ( P, XOther) dp F P ( P, XOther) + e Elast = dy dp P Y = dy / dp / Y P Y / P / Y P = % Y % P

13 Graphical Depiction of a Price Elasticity Y=Quantity Q = P Elast =.125 = Slope = -20/40 = X=Price

14 Price Elasticity - What Does it Mean? Elast = dy dp P Y = dy / dp / Y P Y / P / Y P = % Y % P The Price Elasticity provides the Percentage Change in Y for a small Percentage change in Price. For small price changes this estimate of the price elasticity is accurate, but for large changes the result is misleading.

15 How Do You Account for Price Impacts? Commercial Industrial Outside 11% Other 3% Outside 9% Other 8% End Use 23% Directly 63% End Use 8% Directly 75% Outside 8% Residential Other 5% End Use 18% Directly 69%

16 What Functional Form Do You Use? Don't Use 13% Commercial Other 3% Linear 37% Don't Use 18% Industrial Other 3% Linear 44% Other Source 23% Other Source 14% Diff. Model 3% Double 21% Don't Use 9% Diff. Model 3% Residential Other 4% Linear 41% Double 18% Other Source 21% Diff. Model 4% Double 21%

17 Examples of Including Price Directly Linear Specification Sales = b + b1price + b 2HDD + b3cdd + b 4GDP o + ε Elasticity = dsales d Pr ice Pr ice Sales = b 1 Pr ice Sales

18 Examples of Including Price Directly Double Log Specification Sales = α δ φ γ b o Price HDD CDD GDP + ε ( Sales) = LN( b ) αln( Price) δln( HDD) ϕln( CDD) γln( GDP) ε LN o + Elasticity = α

19 Example of a Structured End-use Variable y,m y,m y CoolUse CoolIndex XCool = = 98, 98, 98, 98,, Pr Pr CDD CDD HHSize HHSize Income Income ice ice CoolUse m y c m y b m y a m y m y = Type Type Type y Type y Type Type y y Eff Sat Eff Sat kwh Index Structural CoolIndex 98 98

20 Do you use Annual, Quarterly, or Monthly Data? Commercial Annual 13% Industrial Annual 29% Quarterly 13% Monthly 55% Monthly 74% Residential Annual 31% Quarterly 16% Monthly 58% Quarterly 11%

21 What Price Variables are Used? Commercial Industrial Fixed Level 3% Other 12% Fixed Level 3% Other 14% Tariff Index 10% Tariff Index 10% Avg Rev 75% Avg Rev 73% Fixed Level 5% Tariff Index 8% Other 12% Residential Avg Rev 75%

22 Do You Use Real or Nominal Terms? Nominal 14% Commercial Both 7% Nominal 11% Industrial Both 6% Real 79% Real 83% Nominal 13% Residential Both 8% Real 79%

23 If Average Revenue, Which Do You Use? Other 24% Commercial Current Month 27% Other 31% Industrial Current Month 28% Moving Average 33% Other 24% Lag Month 16% Residential Moving Average 28% Current Month 27% Lag Month 13% Moving Average 30% Lag Month 19%

24 Do You Capture Long & Short Price Effects? Commercial Industrial No 46% Yes 54% No 52% Yes 48% Residential No 46% Yes 54%

25 How Do You Estimate Long-term Price Impacts? Other 40% Commercial Dist Lag 38% Other 49% Industrial Dist Lag 32% Lag Dep 22% Other 41% Residential Dist Lag 41% Lag Dep 19% Lag Dep 18%

26 Short-term Electric Price Elasticities Residential Commercial Industrial Low Average High

27 Long-term Electric Price Elasticities Residential Commercial Industrial Low Average High

28 Short-term Gas Price Elasticities Residential Commercial Industrial Low Average High Note: Results for the gas sector are based on small samples and as such can not be considered as statistically significant.

29 Long-term Gas Price Elasticities Residential Commercial Industrial Low Average High Note: Results for the gas sector are based on small samples and as such can not be considered as statistically significant.

30 Have the Elasticities Changed Over Time? Commercial Higher 17% Industrial Higher 19% Lower 6% Don t know 46% Lower 12% Don t know 52% Same 25% Residential Higher 18% Same 23% Lower 3% Don t know 53% Same 26%

31 Elasticity Constant or Dependent on the Price? Commercial Industrial Dependent 28% Dependent 29% Constant 72% Constant 71% Residential Dependent 29% Constant 71%

32 Do You Include Cross-price Elasticities? Commercial Yes 25% Industrial Yes 29% No 75% Residential No 71% Yes 29% No 71%

33 Conclusions Average residential natural gas prices are at an all time high. This run up in natural gas prices have renewed interest in quantifying the sensitivity of sales to price changes. In contrast, average residential electric prices are at an all time low. As a result, there is not the same level of concern to address price response on the electric side as there is on the gas side. The main method for estimating price elasticities are statistical models that incorporate prices directly into the model. Linear and Double Log are the most commonly used specifications. The range of price elasticities reported by the survey respondents are consistent with the values found in the literature from the 70 s and 80 s.

34 Questions? Press *1 to ask a question on the phone or type in the box at the bottom, right corner. Next Brown Bag Seminar: Estimating Unbilled Energy - Tuesday, June 6 Upcoming Itron Workshops, Meetings and Case Studies Forecasting April in Washington, DC Fundamentals of MetrixND - May 8-9 in Chicago, IL NEW Using the Visual Basic Application Module in MetrixND - June in Houston, TX or forecasting@itron.com