Combining Multi Wavelet and Multi NN for Power Systems Load Forecasting

Abstract

In the paper, two pre-processing methods for load forecast sampling data including multiwavelet transformation and chaotic time series are introduced. In addition, multi neural network for load forecast including BP artificial neural network, RBF neural network and wavelet neural network are introduced, too. Then, a combination load forecasting model for power load based on chaotic time series, multiwavelet transformation and multi-neural networks is proposed and discussed in the paper. Firstly, the training sample is extracted from power load data through chaotic time series and multiwavelet decomposition. Then the obtained data is trained through BP network, RBF network and wavelet neural network. Lastly, the trained data from three neural networks are input a three-layer feedforward neural network based the variable weight combination load forecasting model. Simulation results show that accuracy of the combination load forecasting model proposed in the paper is higher than any one sole network model and the combination forecast model of three neural networks.