{"title":"Energy Performance of Building Refurbishments: Predictive and Prescriptive AI-based Machine Learning Approaches","authors":"","doi":"10.1016/j.jbusres.2024.114821","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":15123,"journal":{"name":"Journal of Business Research","volume":null,"pages":null},"PeriodicalIF":10.5000,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Business Research","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0148296324003254","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
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.
建筑物的能源性能(EP)对于欧洲各国政府在 2050 年前实现其去碳化目标至关重要。欧盟(EU)的政策为公民主导的建筑改造提供补贴,在此背景下,确定这些改造在显著提高建筑能效(EP)方面的功效势在必行。本研究利用六种基于人工智能的机器学习(ML)算法来识别关键的预测因素,并制定措施来提高建筑翻新后的节能效果。作为最有效的预测模型,梯度提升模型的准确率高达 84.34%,优于其他 ML 模型。此外,对实验中众多预测因素的分析表明,实施现代节能供暖系统、优化住宅特性、定期维护、投资高性能隔热材料以及了解居住者的动态,都是高效节能策略的相关处方。这些结果应能帮助市场参与者就 EP 翻新做出最佳决策。
期刊介绍:
The Journal of Business Research aims to publish research that is rigorous, relevant, and potentially impactful. It examines a wide variety of business decision contexts, processes, and activities, developing insights that are meaningful for theory, practice, and/or society at large. The research is intended to generate meaningful debates in academia and practice, that are thought provoking and have the potential to make a difference to conceptual thinking and/or practice. The Journal is published for a broad range of stakeholders, including scholars, researchers, executives, and policy makers. It aids the application of its research to practical situations and theoretical findings to the reality of the business world as well as to society. The Journal is abstracted and indexed in several databases, including Social Sciences Citation Index, ANBAR, Current Contents, Management Contents, Management Literature in Brief, PsycINFO, Information Service, RePEc, Academic Journal Guide, ABI/Inform, INSPEC, etc.