Energy Performance of Building Refurbishments: Predictive and Prescriptive AI-based Machine Learning Approaches

Christian Gnekpe, Dieudonné Tchuente, Serge Nyawa and Prasanta Kumar Dey

Journal of Business Research, 2024, vol. 183, issue C

Abstract: The energy performance (EP) of buildings is critical for European governments to meet their decarbonization targets by 2050. In the context of European Union (EU) policies, which subsidize citizen-led building renovations, it is imperative to ascertain the efficacy of these renovations in significantly enhancing EP. This study relies on six AI-based machine learning (ML) algorithms to identify key predictors and prescribe measures for enhancing post-renovation EP in building refurbishments. The gradient boosting model outperforms the other ML models with an accuracy rate of 84.34 % as the most effective predictive model. Moreover, an analysis of numerous predictors in the experiment showed that implementing modern energy-efficient heating systems, optimizing dwelling characteristics, regular maintenance, investing in high-performance insulation materials, and understanding the dynamics of the occupants were relevant prescriptions for efficient energy-saving strategies. The results should enable market actors to make optimal decisions regarding EP refurbishments.

Keywords: Energy efficiency; Machine learning; Artificial intelligence; Predictor importance open data; France (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0148296324003254
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:183:y:2024:i:c:s0148296324003254

DOI: 10.1016/j.jbusres.2024.114821

Access Statistics for this article

Journal of Business Research is currently edited by A. G. Woodside

More articles in Journal of Business Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().