Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm

Abstract

Short-term load forecast (STLF) is a key issue for operation of both regulated power systems and electricity markets. In spite of all performed research in this area, there is still an essential need for more accurate and robust load forecast methods. In this paper, a new hybrid forecast method is proposed for this purpose, composed of wavelet transform (WT), neural network (NN) and evolutionary algorithm (EA). Hourly load time series usually consists of both global smooth trends and sharp local variations, i.e. low- and high-frequency components. WT can efficiently decompose the time series into its components. Each component is predicted by a combination of NN and EA and then by inverse WT the hourly load forecast is obtained. The proposed method is examined on three practical power systems and compared with some of the most recent STLF methods.