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Journal Articles Energy and Buildings Year : 2024

Enhancing buildings' energy efficiency prediction through advanced data fusion and fuzzy classification

Abstract

This study proposes a method to predict buildings' energy efficiency based on available descriptive information and without a physical visit, by merging diverse datasets and employing advanced classification techniques. By integrating geographical, structural, legal, and socio-economic data with Energy Performance Certificate (EPC) observations, our approach yields a rich learning set. Through variable selection methods like forward selection with KNN and simultaneous perturbation stochastic approximation for fuzzy KNN, we refine model variables. Comparing fuzzy and hard classification using KNN, Kriging or Random Forest approaches, we find fuzzy classification more adept at capturing nuanced energy inefficiency indicators. Our study highlights the importance of mass energy efficiency prediction for sustainable renovation efforts.
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hal-04525194 , version 1 (28-03-2024)

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Marc Grossouvre, Didier Rullière, Jonathan Villot. Enhancing buildings' energy efficiency prediction through advanced data fusion and fuzzy classification. Energy and Buildings, 2024, 313, pp.114243. ⟨10.1016/j.enbuild.2024.114243⟩. ⟨hal-04525194⟩
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