Neural-wavelet Approach for Short Term Price Forecasting in Deregulated Power Market

Abstract

This paper proposes designing a model using artificial neural network (ANN) and wavelet techniques to increase the accuracy of short term price forecast in the electricity market. The prior electricity price data are treated as time series. They are decomposed into several wavelet coefficient series using the wavelet transform technique known as Discrete Wavelet Transform (DWT), while the forecast model is based on wavelet multi-resolution (MR) decomposition. The wavelet coefficient series are used to train the artificial neural network and used as the inputs to the ANN for electricity price prediction. The Scale Conjugate Gradient (SCG) algorithm is used as the learning algorithm for the ANN. To get the final forecast data, the outputs from the ANN are recombined using the same wavelet technique. The model was evaluated with electricity price data of New South Wales Australia for the year 2008. Empirical results indicate that the WT-ANN combination model improves the price forecasting accuracy.