Assessment of building renovations using Ensemble Learning

Sammanfattning: In the context of global warming, to reduce energy consumption, an unavoidable policy is to renovate badly-isolated buildings. However, most studies concerning efficiency of renovation work do not rely on energy data from smart meters but rather on estimates. To develop a precise tool to assess the quality of renovation work, several ensemble models were tested and compared with existing ones. Each model learns the consumption habits before the date of the works and then predicts what the energy load curve would have been if the works had not been realized. The prediction is finally compared to the actual energy load to infer the savings over the same dataset. The models were compared using precision and time complexity metrics. The best ensemble model’s precision scores are equivalent to the state-of-the-art. Moreover, the developed model is 32 times quicker to fit and predict.