Deep Belief Networks for Electricity Price Forecasting

Abstract

In this paper, one of the aspects of the smart grids is analyzed. This aspect includes the utilization of the large amount of available digital information for creating smart models for planning and forecasting. The latest and new achievements in the field of machine learning are used for that purpose. Specifically, models based on deep belief networks are developed within this paper and it is examined whether these models may be applied for electricity price forecasting. For that purpose, the hourly data of the prices of the power exchanges in the region of Southeast Europe are used. The obtained results present the advantages of the developed models based on deep belief networks, compared to the traditional neural networks, when applied to electricity price forecasting. To this end, the mean average percent error of the deep belief network model is less than the minimum error of the traditional neural network model in each of the analyzed datasets.