Power system short-term load forecasting based on fuzzy neural network

Abstract

Short-Term Load Forecasting (STLF) is an important operational function in both regulated power systems. This study is concerned with the problem of STLF. Considering the factors such as temperature, date type, weather status, etc, which influence the STLF, a model is set up by dynamic recurrent fuzzy neural network. The fuzzy inference function is realized easily by using a product operation in the network. Introducing local recurrent units to hidden layer, the proposed method can overcome the limit of the traditional BP algorithm. The actual simulation is given to demonstrate the effectiveness of the proposed methods. © Maxwell Scientific Organization, 2013.