A Review of Single Artificial Neural Network Models for Electricity Spot Price Forecasting

Abstract

Electricity price forecasting plays a crucial role in a liberalized electricity market. In terms of forecasting approaches, computational intelligence based models have been widely used with respect to electricity price forecasting and among all computation intelligence based models, artificial neural networks are most popular among researchers due to their flexibility and efficiency in handling complexity and non-linearity. However, a review of recent applications of neural networks for electricity price forecasting is not found in the literature. The motivation of this paper is to fill this research gap. In this study, existing approaches are analyzed and a summary of the strengths and weaknesses of each approach is presented. Besides, each neural network model is briefly summarized, followed by reviews of the corresponding studies of each neural network with respect to electricity forecasting from year 2010 onwards. Major contributions, datasets adopted as well as the corresponding experiment results are analyzed for each reviewed study. Apart from the review of existing studies, the advantages and disadvantages of each type of neural network model are discussed in details. Compared with neural networks based hybrid models, a single neural network model is easier to be implemented, less complex and more efficient. Scope of the review is the application of non-hybrid neural network models. It is found that most literature focuses on short term electricity price forecasting while medium and long term forecasting still remain relatively uncovered.