Forecasting electricity prices for a day-ahead pool-based electric energy market

This paper considers forecasting techniques to predict the 24 market-clearing prices of a day-ahead electric energy marker. The techniques considered include time series analysis, neural networks and wavelets. Within the time series procedures, the techniques considered comprise ARIMA, dynamic regression and transfer function. Extensive analysis is conducted using data from the PJM Interconnection. Relevant conclusions are drawn on the effectiveness and flexibility of any one of the considered techniques. Furthermore, they are exhaustively compared among themselves.