Machine Learning-Based Energy Forecasting for Energy Management in Renewable Energy Communities

Renewable Energy Communities (RECs) are crucial in advancing decentralised and sustainable energy systems. Accurate forecasting of short-term electricity demand is essential to support REC operational planning, improve energy efficiency and reduce dependence on external supply. This study presents a practical and effective forecasting framework for hourly electric load in a residential REC in Loureiro, Portugal. The approach is based exclusively on exogenous variables like weather conditions and calendar-based features. Two tree-based machine learning models, Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost), were applied to predict REC electric energy demand. The performance of the models was evaluated using standard regression metrics, including the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R2). Both models achieved high accuracy on unseen data, with CatBoost yielding significantly better results: MAE, RMSE, and R2 are equal to 1.21 kWh, 2.31 kWh, and 0.9718, respectively. An intraday 24-hour comparison confirmed the model's ability to capture intraday consumption dynamics. These findings highlight the potential of exogenous feature-driven forecasting approaches to support efficient energy management in communityscale systems.