Ensemble Machine Learning Modelling for Medium to Long Term Energy Consumption Forecasting

Abstract

Electricity demand forecasting is an important research area that is gaining popularity among researchers these days. Forecasting energy consumption is a critical task as it affects the overall functioning of the power industry. There are many types of research already done in the field of short-term energy forecasting, but there is a scarcity of researches in medium to long-term energy forecasting. This paper focuses on medium to long-term energy forecasting using machine learning and the ensemble approach. The machine learning methods include Linear Regression (LR), Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Gradient Boosting Regressor (GBR). For the comparative analysis the performance metric selected are R2, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE)..