A meta-learning classification model for supporting decisions on energy efficiency investments

Energy efficiency is critical for meeting global energy and climate targets, requiring however significant investments. Due to the lack of mature decision-support systems and the utilization of traditional investment mechanisms that focus on the economical aspects of the energy efficiency projects and neglect their environmental impact, such projects can experience difficulties in being funded. In the interim, the impact of the digitization era is more apparent than ever, as algorithms and data availability and quality have significantly improved. This study aspires to bridge the gap in energy efficiency financing with the development of a data-driven methodology that labels energy efficiency investments based on their expected utility in terms of renovation cost and energy savings. Various machine learning classification methods are deployed and combined through a meta-learning model with the objective to improve overall classification performance and determine the funding that each investment should receive according to its particular characteristics. The proposed methodology is evaluated using a set of 312 projects that have been completed in Latvia. Our results indicate that the meta-learner outperforms all baseline classifiers, effectively identifying projects of high and medium potential and successfully distinguishing low from high potential ones.