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.
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