A generalized knowledge-based short-term load-forecasting technique

A recently developed algorithm for short-term load forecasting is generalized. The algorithm combines features from knowledge-based and statistical techniques. It is based on a generalized model for the weather-load relationship which makes it site-independent. Weather variables are investigated, and their relative effect on the load is reported. The algorithm is also fairly robust and inherently updatable, and it provides a systematic method for operator intervention if necessary. This property makes it especially suitable for application in conjunction with demand side management (DSM) programs. The algorithm uses pairwise comparison to quantify categorical variables, and then utilizes regression to obtain the least-squares estimation of the load. The technique has been tested using data from four different sites in Virginia, Massachusetts, Florida, and Washington. The average absolute weekday forecast errors range from 1.22% to 2.7% over all four seasons in a year.<
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