* Project over a ten year time horizon,

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1 FORECASTING RESIDENTIAL, COMMERCIAL, AND INDUSTRIAL GAS DEMAND Jeffrey P. Brand, Wisconsin Gas Company James D. Funk, Wisconsin Gas Company ABSTRACT This paper shows how the Wisconsin Gas Company used SAS/ETSU. and to develop a gas sales forecasting system. It emphasizes which SAS Institute Inc. software PROC's were used to complete each model development stage. In particular, the Institute's PROC's that were used to build and evaluate the forecasting system are outlined. Although this paper addresses the process for building and evaluating an econometric model, the process can be adapted to most forecasting techniques. INTRODUCTION The Wisconsin Gas Company (WGC) uses gas sales projections for many reasons. In general, the gas sales projections are used for rate case filing, rate design, and long-range planning. For 1 to 2 years out, the WGC needs gas sales projections for rate filings. For 3 to 5 years out, the WGC needs projections for rate design; and for 5 to 10 years out the WGC needs gas sales projections to plan its capital and labor requirements. Gas sales projections are needed for short-term, mediumterm, and long-term business planning. To provide decision makers with gas sales projections, the WGC has used a variety of trend models. While they have met the decision maker's needs in the past, the WGC is concerned that trend models will not meet future management needs. Management's primary concern is that the trend models will not be able to account for the changes in residential. commercial, and industrial consumption behavior. For example, with natural gas prices rising an average of 18 percent since 1979, WGC's gas customers have become sensitive to the price of gas. To combat rising prices, residential customers have found it cost effective to insulate their homes, turn down thermostats, and/or switch to an alternate fuel source. Similarly, industrial customers have found it cost effective to install dual fuel burning boilers - allowing industrial customers to switch to an alternate fuel source literally overnight. This is the type of market environment to which the WGC management has had to adjust, and the gas sales forecasting system must be able to account for. The problem facing the WGC energy forecasting group was to design a new gas sales forecasting system~ To meet WGC's management's needs, the new forecasting system had to: * Produce accurate forecasts, * Account for change in gas consumption behavior, * Measure forecast risks, * Project over a ten year time horizon, * Forecast by Residential, Commercial, and Industrial Rate Class, and * Produce forecasts at monthly time intervals This paper will describe the steps in which the WGC applied SAS Institute software products to develop its gas sales forecasting system. The scope of the paper is to show the process the WGC used for building the gas demand models, and for evaluating the gas demand model's forecasting capabilities. OVERVIEW OF WGC GAS SALES FORECASTING SYSTEM One feature of the forecasting system is its planning capabilities. In particular, the system accounts for four types of market forces which affect the demand for natural gas. These forces are: the economy, energy prices, weather, and the number of customers. To forecast gas demand, consequently, assumptions must first be made about the economic activity, energy prices, weather, and number of customers. Figure 1 summarizes the type of information that is needed to generate gas demand forecasts. FIGURE FORECASTING INFORMATION FLOW GAS DEMAND FORECAST IWIKtJ sae F'ORECAS These gas demand assumptions are produced with a variety of forecasting techniques. They range from a 30 year moving average model used to forecast assumption weather, to an elaborate econometric model used to forecast local economic activity. The Institute's software products were applied to develop a model to forecast energy prices, market size, and economic activity. WGC GAS SALES FORECASTING TECHNIQUE After conducting a thorough model selection procedure, the WGC decided to build a forecasting system based on the principles of econometrics. While the WGC considered other forecasting techniques, the econometric forecasting system most closely met management's needs and resource constraints. This new system could: 92

2 assumption is that the consumer chooses among the available alternatives in such a manner that the satisfaction derived from consuming commodities is as large as possible. Based on this theory, the following variables were chosen to be included in the residential gas demand model. * Tastes Consumer's preference for natural gas * Income - Consumer's budget constraints * Prices - Consumer's decision depends on the price of natural gas, the price of complementary goods (e.g. cost of gas appliances), and the price of substitute fuels (e.g. oil, electricity) * Market Size - The number of customers affects the market demand for gas * Climatic Conditions - Outside temperature The Theory of the Firm The commercial and industrial gas sales model is based on mathematical equations that simulate the producer's gas usage decision making process. The commercial and industrial gas demand equations assume that natural gas is an input into the manufacturing/ operation process. Further, it assumes that the quantity of gas used is the amount that minimizes cost subject to an output constraint. Based on the theory of the firm the following variables were included in the commercial and industrial gas demand equations: * Prices of Inputs - Price of natural gas, price of substitute fuels, and the price of other inputs. * Output - Demand for producer's final product * Technological Change - Changes in the technology * Weather - Outside Temperature Once the variables were selected, the data for each variable was collected and prepared to estimate the residential, commercial and industrial gas demand model. - Steps B. and C. Prepare Data and Estimate Model Figure 2 displays the data preparation and estimation activities. It shows which Institute software PROC's were used to complete each activity. I Figure 2 Data Preparation and Estimation Process I Inspect Data(PROC GPLOT) Deseasonalize Data(PROC XII ESTIMATE PARAMETERS Use Ordinary Least Square(PROC SYSREG) Correct for Autocorrelation(PROC AUTOREG) Activity 1: Prepare Data for Model Estimation Once the relevant variables have been selected, the next model building step is to prepare the data for model estimation. This is an important step, since it allows the modeler to examine how the data varies over time. When inspecting the data, the modeler looks for the seasonality, trend, cycle, and outliers in the series. The Institute's PROC GPLOT is used to complete this activity. If the data series has "alot" variation, it is recommended that the points be connected with a line. One common type of variation in most economic series is seasonality or intra-year variation from year to year. By removing the seasonality, non-seasonal data variations can be modeled (e.g. trend and cyclical variation). The Institute's PROe XII is used to deseasonalize the data series. PROC XII contains sufficient OUTPUT options so the deseasonlized data can be stored in a SAS data se-t for use other applications. Activity 2: Estimate Model Parameters After the data has been examined and the data adjustments made (if necessary), the model is ready to be estimated. When estimating the gas demand models, the goal is to measure how a change in the gas demand factors (economic activity. weather, etc) changes the demand for gas. The Institute's PROe SYSREG and FROC AUTOREG were used to estimate the residential, commercial, and industrial gas demand model. Both PROC SYSREG and PROC AUTOREG contain OUTPUT options which create SAS data sets to store the parameter estimates and forecast errors for use in other applications. Step D. Estimation Evaluation Process After the residential. commercial, and industrial gas demand models are estimated, the estimated models must be evaluated. The objective is to determine if the equations realistically simulate gas customer demand behavior, and to test if any modeling assumptions have been violated. Figure 3 summarizes activities within the estimation evaluation procedure. 93

3 * Produce accurate forecasts. * Simulate gas customer decision making by rate class, * Quantify forecast risks, and * Generate forecasts at monthly intervals. In summary the econometric modeling technique met all of management's selection criteria within its resource constraints. SAS Institute, Inc. Software Products Used The gas demand econometric model was developed with four SAS Institute Inc. software products: * SAS@ * SAS/ETS# * SAS/GRAPH@ * SAS/FSP@ These products were used in every phase of the model development. They were used from the initial SAS data set creation all the way through to writing the final forecast document. WGC FORECASTING PROCESS The gas demand econometric model was developed by following a three stage forecasting process (Cleary, 1982). The stages are: 1. Design Stage - Includes all premodelling activities 2. Specification Stage - Includes all modelling activities 3. Evaluation Stage - Includes all -forecast evaluation activities. The following provides details about each stage. 1. The Design Stage The design stage encompasses all the premodeling activities. In essence, the model selection activities are conducted here. To select the gas sales forecasting technique(s) at the WGC, the selection criteria first were listed. The selection criteria include seven items: 1) forecasting accuracy, 2) planning capabilities. 3) defensibility, 4) data requirements, 5) cost, 6) data processing requirements. and 7) personnel requirements. After the selection criteria were identified, WGC forecasts users ranked the relative importance of each item on a scale of 1 to 7 (with 1 being most important). At the same time, four alternate forecasting techniques were nominated as possible candidates. They were: 1) trended customer use, 2) econometrics, 3) end-use, and 4) time-series. A panel of forecasting experts then ranked each forecasting techniques by the selection criteria. To select the forecasting techniques(s), the users selection criteria rankings were matched with the experts' ranking of each technique. The technique that most closely matched the user rankings with the experts' rankings was chosen. Based on this selection process it was recommended that an econometric forecasting system be designed. Once the forecasting technique was selected the actual model building was ready to begin. The following describes the manner in which SAS Institute, Inc. software products were allied to build residential, commercial, and industrial gas demand models. 2. The Specification Stage The specification stage includes the all the model building steps. The discussion below describes the steps the WGC followed to build its econometric model. This process is divided into four steps: Step A. Applied Demand Theory - Select Relevant Causal Variables Step.B. Prepare the Data - Adjust Raw Data (if necessary) Step C. Estimate the Model Parameters - Estimate model Step D. Evaluate Parameter Estimates - Test estimated relationships for reasonableness Each model building step is detailed below. Step A. Apply Demand Theory To build the gas sales model the factors that have affected gas demand in the past and are likely to affect gas demand in the future must be first identified. To select the gas demand factors, the economic principles of demand were applied. Below is a description of the theory underlying the residential, commercial, and industrial gas demand models. The three gas demand models are based on two economic theories of demand. For the residential customers. the gas demand model is based on the theory of the consumer; and for the commercial and industrial customers, the two gas demand models are founded on the theory of the firm. The assumptions made by both these demand theories are highlighted below. The Theory of the Consumer The residential gas sales model is based on mathematical equations that simulate the process in which customers decide to purchases gas. The equations replicate the buyer/ demanders decision making process. To select the important variables in the buyers decision making process, the neo-classical theory of the consumer is applied. The theory's basic 94

4 Figure 3 Estimation Evaluation Process I CHECK PARAMETER ESTIMATES I Sign(PROC SYSREG and AUTOREG OUTPUT) Sensitivity CHECK SUMMARY STATISTICS R2(PROC SYSREG and AUTOREG OUTPUT) F ( " ) t ( " ) Inspect Residuals(PROC GPLOT) Test for Independence(PROC ARIMA) Test for Normality(PROC UNIVARIATE) Activity 1: Check Parameter Estimates The first activity is to examine the parameter estimates themselves. The objective is to evaluate whether the estimated equations realistically replicate residential, commercial, and industrial gas demand behavior. To determine this, two tests are conducted. Test 1: Check Parameter Sign The parameter sign is first inspected. The objective is to determine if the expected sign was estimated. For example, a positive parameter estimate is expected between the demand for gas and outside temperature. In other words, the colder the weather the greater the demand for gas. If the parameter estimates do not have the expected sign the model must be re-specified. Test 2: Check parameter Sensitivity The parameter sensitivity is tested next to determine if the expected parameter sensitivity was estimated. For example, outside temperature is expected to explain most of the variation in residential gas demand. Test are conducted to measure how much variation in residential gas demand is explained by weather parameter estimates. In a similar manner, industrial gas demand is expected to be very sensitive to the price of oil--a very small drop in the price of oil can cause a significant drop in industrial gas demand. Tests are conducted to determine how much variation in industrial gas demand is explained by the oil price parameter. Activity 2: Check Summary Statistics Next the summary statistics -are examined - F, t, and R2. These tests collectively summarize how well the gas demand factors explained gas demand. The Institute's PROC SYSREG and PROC AUTOREG automatically produce these summary statistics. I Activity 3: Analyze the Forecast Errors The primary objective is to test whether any modeling assumptions have been violated. Three tests are conducted to determine if the forecast errors have constant variance, independence, and are normally distributed. The Institute's software PROC's used to test the three modeling assumptions are noted below. Test 1: Determine if Errors have Constant Variance This test is to determine if the residuals have a constant variance around a zero forecast error. The Institute's PROC GPLOT is used here. In this case, the forecast errors are plotted against time. The goal of plotting the residual is to visually determine whether the forecast errors are increasing or decreasing with time. If they are, then the model must be re-specified. Test 2: Determine if Errors are Independent The Institute's PROC ARlMA is used to test if the forecast errors are independent of each other. When using PROC ARIMA for this purpose, it is only necessary to use IDENTIFY statement. The correlogram, output from PROC ARIMA, is used to test if the residuals are significantly related to other residuals one or more lags apart. Test 3: Determine if Errors are Normally Distributed The Institute's PROC UNIVARIATE is used to test whether errors are normally distributed. When using_ PROC UNIVARIATE'S NORMAL option, the quantile-quantile (Q-Q) plot is outputed. The Q-Q graph plots the forecast error quantiles against the normal distribution quantiles. If the forecast errors are normally distributed, there will be a linear line between the residual quantiles and the normal distribution quantiles. In summary, the modeling process can proceed to the forecast evaluation stage if all parameter estimates realistically describe gas demand behavior, the summary statistics indicate a good fit, and the modeling assumptions have not been violated. 3. The Forecast Evaluation Stage The objective of this stage is to make a forecast, measure its risks, gain management's approval of the forecast, and then to monitor the forecasts. This process is summarized in Figure 4. 95

5 J Figure 4 Estimation Evaluation Process I PRODUCE FORECASTS MEASURE FORECAST RISKS Perform Year Ahead Test Generate Confidence Limits(PROC MATRIX) Perform Sensitivity Analysis(PROC FSEDIT) I PRESENT FORECASTS(PROC GPLOT,PROC PRINT) MONITOR FORECAST Track Forcasts(PROC GPLOT) Track Model Track Assumptions(PROC GPLOT) A panel driven system is used to generate the gas demand forecasts. Using the ISPF dialog service and CLIST~ the panels allow the casual user to produce gas demand forecasts as well as increase the productivity of the modeler. The panels use PROC FSEDIT to change/update the gas demand forecast assumptions. Step 1: Produce the Forecast First the residential, commercial, and industrial gas demand forecasts are made. To generate the forecasts~ assumptions about the local economy, energy prices, weather, and market size must be made. The Institute's PROC FSEDIT is used to update the forecast assumptions. Step 2: Measure the Forecast Risks Once the gas demand forecasts have been produced, the forecast's risks are measured. To do so three test are conducted. Test 1: Produce Year Out Test The residential, commercial~ and industrial gas demand forecasts are generated outside the estimation period. For example the models might be estimated from ; then use it to produce forecasts for 1983 and The objective is to measure the model's forecasting accuracy outside the estimation. Test 2: Generate Confidence Intervals When a forecast is given~ the confidence limit is also given. That is, the probability of the point forecast being between two numbers is.generated. This allows the decision makers to assess the risk of using any particular forecast. The Institute's PROe MATRIX is used for this test. Test 3: Perform Sensitivity Analysis The objective of the sensitivity test is to measure the impact of making an incorrect gas demand assumption. For example, suppose the WGe assumes the economic-recovery will continue~ but the economy actually has a recession. The impact of making the wrong gas demand assumption is assessed. Using the panel driven system~ the Institute's PROt FSEDIT is used to change the gas demand assumptions. Step 3: Present the Forecast The approach to presenting the gas demand forecasts to the decision makers is to sell the ~orecasts. To do so, the forecasting accuracy of the gas demand models is emphasized. The presentation is made as non-technical as possible. The Institute's PROC GPLOT and PROC PRINT are very useful for meeting this objective. ~: Monitor Forecast The objective of monitoring the forecast is to be able to determine if the model is replicating residential, commercial. and industrial gas demand behavior. By doing this the gas demand forecasts, gas demand models, and gas demand assumptions are tracked. The Institute's PROe GPLOT is very useful for tracking the gas demand forecasts and gas demand assumptions. The goal of this step is to test the hypothesis that gas demand behavior has changed. CONCLUSION The process the WGe used to apply SAS Institute. Inc. 's software products to develop a gas sales forecasting system has been delineated. In particular~ the emphasis was' given to the methodology for building and evaluating the residential~ commercial~ and industrial gas demand forecasts. To complete the model building and evaluation stages, the WGC feels SAS Institute. Inc.'s software products have met all of its data base, modeling~ and report writing needs. Further, as the system grows to include other forecasting techniques, the WGC feels SAS Institute Inc. will meet its future modeling needs as well. REFERENCES Cleary, James and Levenbach. Hans. The Professional Forecaster (Belmont CA: Lifetime Learning Publications, 1982) Henderson~ James and Quandt. Richard. Microeconomic Theory: A Mathematical Approach (New York: McGraw-Hill Book Co. 1980) Theil~ Henri. Principles of Econom~trics (New York: John Wiley & S,ons, Inc. 1971) 96

6 SAS, SAS/GRAPH, AND SAS/FSP are registered trademarks of SAS Instutute Inc., Cary, NC, USA. Symbol for Registered SAS/ETS is a trademark of SAS Institute Inc. Cary, NC, USA. Symbol for Trademark = n. Author: Jeffrey P. Brand Wisconsin Gas Company 626 E. Wisconsin Ave. Milwaukee, WI (414)