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A new method of extrapolating feeder peak load histories to produce estimates of future feeder bads is described. The method, an improvement on past multiple regression curve fit methods, uses an "assumed" geometry based on substation locations and a classification by recent growth rates to group feeders into six classes, each extrapolated in a slightly different manner. The new method is Simple enough to be applied in situations where computing resources are limited. A series of tests that the new method outperforms other distribution load extrapolation methods, and that for short range (less than five Years ahead) forecasts, it matches the accuracy of simulation forecasting methods, which require considerably more data and computer resources.

Utilities are required to provide reliable power to customers. In the design stages, utilities need to plan ahead for anticipated future load growth under different possible scenarios. Their decisions and designs can affect the gam or loss of millions of dollars for their companies as well a s customer satisfaction and future economic growth in their territory. This paper proposes and describe the general methodology to use fuzzy logic to fuse the available information for spatial load forecasting. The proposed scheme can provide distribution planners other alternatives to aggregate their information for spatial load forecasting.