Electricity price forecasting using neural networks with an improved iterative training algorithm

In a competitive electricity market, the forecasting of energy prices is an important activity for all market participants either for developing bidding strategies or for making investment decisions. In this article, a new forecasting strategy is proposed for short-term prediction of the electricity price, which is a complex quantity with nonlinear, volatile and time-dependent behaviour. Our forecast strategy includes two novelties: a new two-stage feature selection algorithm and a new iterative training algorithm. The feature selection algorithm has two filtering stages to remove irrelevant and redundant candidate inputs, respectively. This algorithm is based on mutual information and correlation analysis. The improved iterative training algorithm is composed of two neural networks in which the output of the first neural network is one of the inputs to the second. The overall proposed strategy is applied to the Pennsylvania-New Jersey-Maryland (PJM) electricity markets and compared with some of the most recent price forecast methods.