A novel algorithm for energy market risk forecasting

Abstract

This paper proposes a novel artificial intelligence based approach for the accurate forecasting of the market cost incorporating the error risk. The proposed model makes use of a hybrid evolving solution based on firefly algorithm and support vector regression (SVR) for getting to the highest accuracy and precision. In contrast to the artificial neural network models which face the overfitting problem, the SVR would keep a limit on the forecast model complexity and thus would escape from the overfitting concerns. In order to get into the maximum performance of the SVR, one need to adjust the hyperparameters optimally. This article proposes the firefly algorithm which mimics the social behavior of these insects in their colony. In addition, a new modification method is introduced which would add up the algorithm population diversity and thus enhance the search results. The appropriate performance of the proposed hybrid AI base model is assessed on the typical market price datasets.