Hybrid WT-PSO based Neural Networks for Single Step-Ahead Wind Power Prediction for Ontario Electricity Market

Abstract

Wind Power forecasting is an important subject of concern for reliable operations of grid and it has been studied from different points of views of both accuracy and reliability. So with an aim of improvement in prediction accuracy this paper presents a hybrid wind power prediction machine for Ontario Electricity Market (OEM) on single step ahead basis in which Wavelet Transform (WT) is used for pre- processing of input wind power data, then the pre-processed data is trained by neural networks. In this initially, the parameters of neural networks (biases & weights) are initialized as random &then at second stage are optimized by Particle Swarm Optimization (PSO) base training algorithm. The varying time series input training data patterns are used in order to remove the overtraining & over-fitting problem so that the maximum accuracy is achieved. The results of proposed method are compared with Naive Predictor, Feed Forward Neural Networks (FFNN) & Particle Swarm Optimization based Neural Network (PSONN) and is presented in the form of comparative tables on Mean absolute error (MAE) & mean absolute percentage error (MAPE) scale with emphasis on weekly as well as monthly predictions. The data used by proposed model for estimation is collected from Ontario Electricity Market for the year 2009-12 and tested for such a long period of one year on single step ahead basis. It is found that the accuracy of proposed model is far better than the other models.