Electricity Price Forecasting Based on Enhanced Convolutional Neural Network in Smart Grid

Abstract

Electricity price forecasting is significant component of smart grid. Electricity systems are managed by the electricity market. The market operators perform electricity price forecasting for an efficient energy management. This paper deals with the electricity price forecasting based on deep learning. The fluctuations in electricity prices are due to the increase in fuel prices, demand of electricity and social variables such as weather conditions, peak hours, weekdays, weekends and seasons. Therefore, there is a need to maintain equilibrium between shortage and overflow of the electricity. Deep learning is most widely used for classification, image recognition and forecasting. The proposed work is categorized into two stages: first stage is feature engineering, in which features selection is performed by Xgboost technique, while features extraction is done through Linear Discriminant Analysis (LDA). These techniques reduce the dimensionality of data and forward important data to classifier for electricity price forecasting. Second stage is price forecasting, which is based on Enhanced Convolutional Neural Network (ECNN) classifier. For validation of proposed work, three performance metrics (i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE)) are used. Simulation results show that our proposed scheme outperforms existing benchmark techniques in terms of price forecasting.