Next-day peak electricity price forecasting using NN based on rough sets theory

This paper proposes an approach for next-day peak electricity price forecasting using neural networks (NN), based on rough sets. In the proposed method, input factors of the NN are selected by using correlation analysis. Moreover, learning data used for training of the NN, is selected by rough sets. The proposed method for creating learning data based on temperature fluctuation is used for generation of new learning data. The proposed method is examined by using the data of PJM electricity market. From the simulation results, it is observed that the proposed method is useful for next-day peak electricity price forecasting.