Data-driven MLmodels for accurate prediction of energy consumption in a low-energy house: A comparative study of XGBoost, Random Forest, Decision Tree, and Support Vector Machine

Abstract

Residential building energy consumption is a significant contributor to greenhouse gas emissions. Accurate prediction of total energy use in residential buildings holds vital importance in the context of energy management. In this paper, we propose a data-driven approach using machine learning (ML) models to predict the total energy consumption of a low-energy house based on indoor and outdoor environmental conditions using data from a house located in Belgium. Four ML(ML) models, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM), were trained and tested to evaluate their performance in predicting energy consumption. The results of our study demonstrate that the XGBoost model outperforms all other models used , with a coefficient of determination R² of 61%, a Root Mean Square Error (RMSE) of 65.28, a Mean Absolute Error (MAE) of 29.81, and a Mean Absolute Percentage Error (MAPE) of 28.55 on the testing set. The findings from this study demonstrate the accurate forecasting of energy consumption by accounting for the non-linear dependencies between environmental conditions and total energy consumption, which can aid in making informed decisions towards the reduction of power usage, enhancement of energy efficiency, and achieving cost savings.