A Deep Learning Approach Towards Price Forecasting Using Enhanced Convolutional Neural Network in Smart Grid

Abstract

In this paper, we attempt to predict short term price forecasting in Smart Grid (SG) deep learning and data mining techniques. We proposed a model for price forecasting, which consists of three steps: feature engineering, tuning classifier and classification. A hybrid feature selector is propose by fusing XG-Boost (XGB) and Decision Tree (DT). To perform feature selection, threshold is defined to control selection. In addition, Recursive Feature Elimination (RFE) is used for to remove redundancy of data. In order, to tune the parameters of classifier dynamically according to dataset we adopt Grid Search (GS). Enhanced Convolutional Neural Network (ECNN) and Support Vector Regression (SVR) are used for classification. Lastly, to investigate the capability of proposed model, we compare proposed model with different benchmark scheme. The following performance metrics: MSE, RMSE, MAE, and MAPE are used to evaluate the performance of models.