Electricity Price Forecasting Using Neural Networks and Similar Days

This chapter focuses on day-ahead forecasts of electricity price in the PJM market using artificial neural network (ANN) model based on the similar days (SD) method. The PJM competitive market is a regional transmission organization (RTO) that plays a vital role in the US electric system. The chapter contributes to forecast electricity prices in the day-ahead market. In addition to the integration of SD and ANN method, it also proposes a new technique to forecast hourly electricity prices in the PJM market using a recursive neural network (RNN), which is based on the SD method. The proposed RNN model is also applied to generate the next three-day price forecasts. To evaluate the performance of the proposed neural networks, the mean absolute percentage error (MAPE), mean absolute error (MAE), and forecast mean square error (FMSE) are calculated.