Two types of forecasting methods have been receiving increasing attention by electric utility forecasters. The first type, called end-use forecasting, is recognized as an approach which is well suited for forecasting during periods characterized by technological change. The method is straightforward. The stock levels of energy-consuming equipment are forecast, as well as the energy consumption characteristics of the equipment. The final forecast is the product of the stock and usage characteristics.
This approach is well suited to forecasting long time periods when technological change, equipment depletion and replacement, and other structural changes are evident. For time periods of shorter duration, these factors are static and variations are more likely to result from shocks to the environment. The shocks influence the usage of the equipment. A second forecasting approach using time-series analysis has been demonstrated to be superior for these applications.
This paper discusses the integration of the two methods into a unified system, The result is a time-series model whose parameter effects become dynamic in character. An example of the models being used at the Georgia Power Company is presented. It is demonstrated that a time-series model which incorporates end-use stock and usage information is superior--even in short-term forecasting situations-to a similar time-series model which excludes the information.
KEY WORDS Sales forecasting Electricity Time-series forecasting
End-use analysis
The electric utility industry in the United States imposes great demands upon forecasters and planners. Forecasts of demand and energy sales are used in a wide variety of analyses. For example, when evaluating the future need for generating facilities, forecasts of twelve or more years are required. Because of the different types of generating facilities which can be built, forecasts of total sales or peak demand are no longer sufficient. Today's planning processes can use information about daily a n d even hourly loads.
Another use of forecast information is in rate design and regulatory support. Many regulatory jurisdictions use projected sales and load shapes to design tariffs which reflect the cost of service