Survey of hybrid fuzzy neural approaches to electric load forecasting

Economically efficient operation of electric power systems necessitates close tracking of the overall load by the system generation at all times. A wide range of methodologies and models have been developed over the years to predict the future load with reasonable accuracy and reliability. Several research groups have studied the use of artificial neural networks for this application and reported superior results compared to the conventional approaches. Application of fuzzy systems has also been proposed to include imprecise and probabilistic information in the input data. Synthesis of these two complementary technologies has emerged as a highly promising approach for electric load forecasting. This paper aims to provide an overview of the published literature on this topic, highlighting common features and drawing out some important aspects of the methodology used.