Classification of Day-Ahead Deregulated Electricity Market Prices Using DCT-CFNN

Abstract

Artificial neural networks (ANNs) are promising methods for the pattern recognition and classification. In this paper applies ANN to day-ahead deregulated energy market prices. The optimal profit is determined by applying a perfect price forecast. A price forecast with a less prediction errors, yields maximum profits for market players. The numerical electricity price forecasting is high in forecasting errors of various approaches. In this paper, discrete cosine transforms (DCT) based cascade-forward neural network (CFNN) approach (DCT-CFNN) is used to classify the electricity markets of mainland Spain and New York is presented. These electricity price classifications are important because all market participants do not to know the exact value of future prices in their decision-making process. In this paper, classifications of electricity market prices with respect to pre-specified electricity price threshold are used. In this proposed approach, all time series (historical price series) are transformed from time domain to frequency domain using DCT. These discriminative spectral co-efficient forms the set of input features and are classified using CFNN. It has been observed that features selected from spectral domain improve the classification accuracy. The proposed model is more effective compared to some of the most recent price classification models.