A Fuzzy-Preconditioned GRBFN Model for Electricity Price Forecasting

Abstract

This paper proposes a fuzzy-preconditioned ANN (Artificial Neural Network) model for electricity price forecasting. The deregulated electric power systems have been widely spread to trade electric power through electric power markets. The market players are interested in gaining the inside track to maximize the profits and minimize risks in advance. One of the most important tasks is how to forecast a complicated time series of electricity price that affects transmission network congestion. In practice, it is hard to forecast electricity price with the spikes due to the high nonlinearity. In this paper, GRBFN (General Radial Basis Function Network) of ANN is proposed for electricity price forecasting. It is an extension of RBFN of ANN in a way that the optimal parameters of the Gaussian functions are determined by the learning process in RBFN. To improve the model accuracy of GRBFN, FCE (fuzzy c-Elliptotypes) is introduced into GRBFN as a fuzzy precondition technique. FCE is an extension of FCM (fuzzy c-means) and plays a key role to classify input data into fuzzy clusters. The use of FCE contributes to the improvement of model accuracy for spikes of electricity price that bring about much higher price. This paper makes use of DA clustering to evaluate a better initial solution of the parameters of the Gaussian functions. Also, EPSO of evolutionary computation is used to evaluate better weights between neurons. The proposed method is successfully applied to real data of electricity price.