AbstractTwo 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 exclu