Abstract
Transparent and explainable machine learning (ML) models are essential in various domains, e.g., energy consumption, where decarbonization is the main challenge. The European Union is focusing on energy efficiency retrofits in residential buildings to help reach its 2050 carbon emissions target. The cost of these investments is often a strong factor, requiring decision-makers to understand the motivations driving homeowners’ decisions to undertake energy retrofits. Instead of hedonic models commonly used in operational management research studies, we rely on ML methods to predict homeowners’ decisions to undertake energy retrofits, using data from 51,000 households in France. We describe the data preparation, model training, and evaluation; results show that artificial neural networks outperform other popular ML techniques (91.4%). Our post hoc method based on sensitivity analysis and feature importance contributes to the transparency and interpretability of the results. We show that the type of public aid used, head of household gender, family size, prior knowledge of aid, urban vs. rural area, geographical location, occupancy status, and working status are the most important factors in the decision to undertake energy efficiency retrofits. Our predictive methods help decision-makers to make optimal decisions about the level, type, beneficiaries of public incentives for energy retrofits, and expected outcomes; companies in the construction sector can understand homeowners’ key motivations and optimally calibrate their strategic investments and operations.
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Notes
The dataset is publicly available on the following link: https://www.statistiques.developpement-durable.gouv.fr/sites/default/files/2021-10/tremi_2020_metropole_opda.csv. The corresponding dataset dictionary is also available on the following link: https://www.statistiques.developpement-durable.gouv.fr/sites/default/files/2022-01/tremi_2020_dictionnaire_variables_opda.xlsx
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Nyawa, S., Gnekpe, C. & Tchuente, D. Transparent machine learning models for predicting decisions to undertake energy retrofits in residential buildings. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05217-5
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DOI: https://doi.org/10.1007/s10479-023-05217-5