Big Data Based Electricity Price Forecasting Using Enhanced Convolutional Neural Network in the Smart Grid

Abstract

With the development of renewable energies resources and uncertainties in load time series, it is necessary to predict an accurate price for efficient scheduling and operation of generation that is reliable and reduce the power losses in smart grid. The machine learning algorithms are mostly used for power and price forecasting. However, on large data set, it causes over fitting, computational overhead and complexity. To cope with these challenges, in this paper, an electricity price forecasting model is developed using deep learning technique. The proposed method is composed of three modules. First, to performs better feature selection, a hybrid model composed of Mutual Information (MI) and ReliefF is used. Second, Kernel Principal Component Analysis (KPCA) is performed to avoid feature redundancy. Finally, Enhanced Convolution Neural Network (ECNN) performs the regression. To investigate the performance of the proposed model, the results of the proposed is compared with Multilayer Perceptron (MLP) and Support Vector Machine (SVM) as benchmark schemes. The accuracy results show that the performance of our model is better than benchmark schemes. Our technique is robust and helps in better operation and planning of generation in smart grid.