Energy Consumption of a Building Using the XGBoost Algorithm: A Forecasting Study

Buildings are crucial for achieving decarbonization by 2050. In 2022, they represented 34% of global energy demand and 37% of CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions related to energy and processes. Consequently, there has been significant interest in evaluating building energy efficiency from various perspectives over the past decade. In this paper, machine learning (ML) methods based on a meta-approach known as ensemble learning are proposed to predict the energy consumption of a university building. For this purpose, multivariate time series and multi-step prediction horizons are considered in two scenarios with different timestep resolutions. Ensemble learning methods such as Bagging (Random Forest), Boosting (Extreme Gradient Boosting), and Stacking are analyzed. The results show that Extreme Gradient Boosting achieves the best prediction error metrics RMSE, MAE, and MAPE in both scenarios. These models enhance predictive control systems in buildings for accurate short-term energy consumption forecasts.