A comparative study of different machine learning methods for electricity prices forecasting of an electricity market

Generally, it is difficult to accurately forecast electricity prices because they are unpredictable. Yet, accurate price forecasting is expected to provide crucial information, needed by power producers and consumers to bid strategically, thereby decreasing their risks and increasing their profits in the electricity market. In this paper, two models using artificial neural networks (ANN) and support vector machines (SVM) were developed for electricity price forecasting. In addition, ant colony optimization (ACO) was used to reduce the feature space and give the best attribute subset for ANN model. Using ACO for feature selection significantly reduced the training time for ANN-based electricity price forecasting model while the results were almost as accurate as those from ANN model.