Short-term load forecasting using wavelet networks

This paper describes an application of a type of neural network, called the wavelet network, to short-term load forecasting. Based on the wavelet tran sform theory, the recently developed wavelet network is proposed as an alte rnative to the feed-forward neural networks for approximating arbitrary non linear functions and for solving classification problems. Performance resul ts are given for the daily peak and total load forecasting using data from a real power system. A comparison among results from the wavelet network, t he radial-basis feedforward (RBF) neural network and back-propagation (BP) neural network shows that the wavelet network is encouraging.