Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need

Alberto Barbaresi, Mattia Ceccarelli, Giulia Menichetti, Daniele Torreggiani, Patrizia Tassinari and Marco Bovo
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Alberto Barbaresi: Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy
Mattia Ceccarelli: Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy
Giulia Menichetti: Department of Physics, Northeastern University, Boston, MA 02115, USA
Daniele Torreggiani: Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy
Patrizia Tassinari: Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy
Marco Bovo: Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy

Energies, 2022, vol. 15, issue 4, 1-16

Abstract: Accurate prediction of building energy need plays a fundamental role in building design, despite the high computational cost to search for optimal energy saving solutions. An important advancement in the reduction of computational time could come from the application of machine learning models to circumvent energy simulations. With the goal of drastically limiting the number of simulations, in this paper we investigate the regression performance of different machine learning models, i.e., Support Vector Machine, Random Forest, and Extreme Gradient Boosting, trained on a small data-set of energy simulations performed on a case study building. Among the XX algorithms, the tree-based Extreme Gradient Boosting showed the best performance. Overall, we find that machine learning methods offer efficient and interpretable solutions, that could help academics and professionals in shaping better design strategies, informed by feature importance.

Keywords: machine learning; building energy simulation; optimisation algorithms; building energy saving solutions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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