An Electricity Price Forecasting Model with Fuzzy Clustering Preconditioned ANN

In this paper, a hybrid model of fuzzy clustering and ANN (Artificial Neural Network) is proposed for electricity price forecasting. Due to the complicated behavior of electricity price in power markets, market players are interested in maximizing profits while minimizing risks. As a result, more accurate models are required to deal with electricity price forecasting. This paper proposes a new method that makes use of fuzzy clustering preconditioned GRBFN (Generalized Radial Basis Function Network) to provide more accurate predicted prices. Fuzzy clustering plays a key role to prevent the number of learning data from decreasing at each cluster. GRBFN is one of efficient ANNs to approximate nonlinear systems. Furthermore, a modified GRBFN model is developed to improve the performance of GRBFN with the use of DA (Deterministic Annealing) clustering for the parameters initialization and EPSO (Evolutionary Particle Swarm Optimization) for optimizing the parameters of GRBFN. The proposed method is successfully applied to real data of ISO New England, USA.