A newly-developed technique for short-term load forecasting is generalized. The algorithm combines features from knowledge-based and statistical techniques. The technique 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. That effect is modeled via a set of parameters and rules that constitute the rule based technique. This technique is very close to the intuitive judgmental approach an operator would use to make his guess of the load. That is why it provides a systematic way for operator intervention if necessary. This property makes the technique especially suitable for application in conjunction with demand side management (DSM) programs. Moreover, the algorithm uses pairwise comparison to quantify the categorical variables, and then utilizes regression to obtain the least-square estimation of the load. Because it uses the pairwise comparison technique, it is fairly robust. Since the forecast does not depend on any preset model, the technique is inherently updatable. A generalized version of 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 1.30% to 3.10% over all four seasons in a year. Error distributions show that the errors are 5% or less around 91 % of the time. / Master of Science