Load and Price Forecasting Based on Enhanced Logistic Regression in Smart Grid

Abstract

Smart Grid (SG) is a modern electricity grid that enhance the efficiency and reliability of electricity generation, distribution and consumption. It plays an important role in modern energy infrastructure. Energy consumption and generation have fluctuating behaviour in SG. Load and price forecasting can decrease the variation between energy generation and consumption. In this paper, we proposed a model for forecasting, which consists of feature selection, extraction and classification. With the combination of Fast Correlation Based Filter (FCBF) and Recursive Feature Elimination (RFE) is used to perform feature selection to minimize the redundancy. Furthermore, Mutual Information technique is used for feature extraction. To forecast electricity load and price, we applied Naive Bayes (NB), Logistic Regression (LR) and Enhanced Logistic Regression (ELR) techniques. Our proposed technique ELR beats other techniques in term of forecasting accuracy of load and price. The load forecasting accuracy of ELR, LR and NB techniques are 80%, 82% and 85%, while price forecasting accuracy are 78%, 81% and 84%. Locational Marginal Price of Pennsylvania, Jersey, Maryland (LBM-PJM) market data used in our proposed model. Forecasting performance is assessed by using RMSE, MAPE, MAE and MSE. Simulation results show that our proposed technique ELR is perform better than other techniques.