Electricity Price Forecasting in Smart Grid: A Novel E-CNN Model

Abstract

The vital part of the smart grid is electricity price forecasting because it makes grid cost saving. Although, existing systems for price forecasting may be challenging to manage with enormous price data in the grid. As repetition from the feature cannot be avoided and an integrated system is needed for regulating the plans in price. To handle this problem, a new price forecasting system is developed. This proposed model particularly integrated with three systems. Initially, features are selected from the random data by combining the Mutual Information (MI) and Random Forest (RF). The Grey Correlation Analysis (GCA) is used to remove the redundancy from the selected features. Secondly, the Recursive Feature Elimination (RFE) scheme is used to reduce the dimensions. Finally, classification is done based on Enhanced-Convolutional Neural Network (E-CNN) classifier to forecast the price. The simulation results show that our accuracy of the proposed system is higher than existing benchmark schemes.