Day-ahead price forecasting of electricity markets by a new fuzzy neural network

In this paper, an efficient method based on a new fuzzy neural network is proposed for short-term price forecasting of electricity markets. This fuzzy neural network has inter-layer and feed-forward architecture with a new hypercubic training mechanism. The proposed method predicts hourly market-clearing prices for the day-ahead electricity markets. By combination of fuzzy logic and an efficient learning algorithm, an appropriate model for the nonstationary behavior and outliers of the price series is presented. The proposed method is examined on the Spanish electricity market. It is shown that the method can provide more accurate results than the other price forecasting techniques, such as AWMA time series, wavelet-ARIMA, MLP, and RBF neural networks.