Resumen
Energy efficiency financing is considered among the top priorities in the energy sector among several stakeholders. In this context, accurately estimating the energy savings achieved by energy efficiency actions before being approved and implemented is of major importance to ensure the optimal allocation of the available financial resources. This study aims to provide a machine-learningbased methodological framework for a priori predicting the energy savings of energy efficiency renovation actions. The proposed solution consists of three tree-based algorithms that exploit bagging and boosting as well as an additional ensembling level that further mitigates prediction uncertainty. The proposed models are empirically evaluated using a database of various, diverse energy efficiency renovation investments. Results indicate that the ensemble model outperforms the three individual models in terms of forecasting accuracy. Also, the generated predictions are relatively accurate for all the examined project categories, a finding that supports the robustness of the proposed approach.