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A method of forecasting the heat sensitive portion of electrical demand and energy utilizing a summer weather load model and taking into account probability variation of weather factors is discussed in this paper. The heat sensitive portion of the load is separated from base load and historical data is used to determine the effect of weather on the system load. This method is based on regression analysis of historical load and weather information and the establishment of system load characteristics based on historical or survey information. The method has been determined primarily for forecasting demands and energy for the intermediate range of from 3 to 10 years. However, it is applicable for monthly and annual peak forecasting, but probably not applicable for short terms such as hour to hour or day to day forecast. It may also be helpful in long term forecasting with appropriate forecast of future quantities of heat sensitive load on the system. Separate weather load models are used for determining the heat sensitive portion of electric energy and demand independently. These correlation studies were made when the author was located in Birmingham, Alabama and all of the studies reported here are on systems in that general area. The author hopes to continue these weather-load correlation studies in the northeast area in the near future.