Towards Sustainable Energy: Predictive Models for Space Heating Consumption at the European Central Bank

Space heating consumption prediction is critical for energy management and efficiency, directly impacting sustainability and efforts to reduce greenhouse gas emissions. Accurate models enable better demand forecasting, promote the use of green energy, and support decarbonization goals. However, existing models often lack precision due to limited feature sets, suboptimal algorithm choices, and limited access to weather data, which reduces generalizability. This study addresses these gaps by evaluating various Machine Learning and Deep Learning models, including K-Nearest Neighbors, Support Vector Regression, Decision Trees, Linear Regression, XGBoost, Random Forest, Gradient Boosting, AdaBoost, Long Short-Term Memory, and Gated Recurrent Units. We utilized space heating consumption data from the European Central Bank Headquarters office as a case study. We employed a methodology that involved splitting the features into three categories based on the correlation and evaluating model performance using Mean Squared Error, Mean Absolute Error, Root Mean Squared Error, and R-squared metrics. Results indicate that XGBoost consistently outperformed other models, particularly when utilizing all available features, achieving an R2 value of 0.966 using the weather data from the building weather station. This model’s superior performance underscores the importance of comprehensive feature sets for accurate predictions. The significance of this study lies in its contribution to sustainable energy management practices. By improving the accuracy of space heating consumption forecasts, our approach supports the efficient use of green energy resources, aiding in the global efforts towards decarbonization and reducing carbon footprints in urban environments.