Application of support vector machines and ANFIS to the short-term load forecasting

Abstract

Load forecasting is vitally important for the electric industry in the deregulated economy. Short-term load forecasting (STLF) has always been a very important issue in power system planning and operation. Recently, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. However, forecasting electricity load is difficult because of the randomness and uncertainties of load demand. Many mathematical methods have been developed for load forecasting. In this paper we discuss some methodologies for load forecasting. One set of load forecasting curves are used for make a classification with different techniques as neural networks, fuzzy logic and support vector machines. A comparative analysis is done for each technique and the results present the advantages of each one of them.