Abstract
A hybrid neural network-fully expert system is developed to forecast short-term electric load accurately. The fuzzy membership values of load and other weather variables are the inputs to the neural network and the output comprises the membership values of the predicted load. An adaptive fuzzy correction scheme is used to forecast the final load by using a fuzzy rule base and fuzzy inference mechanism. Extensive studies have been performed for all seasons, and some examples are presented in this paper, which include average, peak, and hourly load forecasts.
Get full access to this article
View all access options for this article.