Enhanced Classification with Logistic Regression for Short Term Price and Load Forecasting in Smart Homes

Abstract

In this paper, an accurate electricity load and price forecasting model has been proposed, which consists of feature engineering and classification. To remove irrelevant features, Decision Tree (DT) and Recursive Feature Elimination (RFE) are used. Features are extracted through Mutual Information (MI) after removing uncertainty. In order to attain accurate electricity load and price forecasting, Enhanced Logistic Regression (ELR) classifier is proposed. Simulation results testify that accuracy of ELR is better than Logistic Regression (LR) and MultiLayer Percepton (MLP). ELR beats LR and MLP by 0.26% and 7.287% in load forecasting, whereas, it outperforms LR and MLP in price forecasting by 1.413% and 3.057%, respectively. Smart* dataset is used, which contains the data of residential sector of Western Massachusetts. Prediction performance is evaluated by using Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).