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dc.contributor.authorEgwim, Christian Nnaemeka
dc.contributor.authorAlaka, Hafiz
dc.contributor.authorEgunjobi, Oluwapelumi
dc.contributor.authorGomes, Alvaro
dc.contributor.authorMporas, Iosif
dc.date.accessioned2022-10-17T15:30:02Z
dc.date.available2022-10-17T15:30:02Z
dc.date.issued2022-09-26
dc.identifier.citationEgwim , C N , Alaka , H , Egunjobi , O , Gomes , A & Mporas , I 2022 , ' Comparison of Machine Learning Algorithms for Evaluating Building Energy Efficiency Using Big Data Analytics ' , Journal of Engineering, Design and Technology . https://doi.org/10.1108/jedt-05-2022-0238
dc.identifier.issn1726-0531
dc.identifier.otherJisc: 592410
dc.identifier.urihttp://hdl.handle.net/2299/25823
dc.description© 2022, Emerald Publishing Limited. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1108/jedt-05-2022-0238
dc.description.abstractPurpose: This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings. Design/methodology/approach: This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics. Findings: Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting. Research limitations/implications: While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK. Practical implications: This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system. Originality/value: This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.en
dc.format.extent26
dc.format.extent926295
dc.language.isoeng
dc.relation.ispartofJournal of Engineering, Design and Technology
dc.subjectOriginal Article
dc.subjectBig Data Analytics
dc.subjectBuildings
dc.subjectBUILDING PERFORMANCE
dc.subjectEnergy Efficiency
dc.subjectMachine Learning
dc.subjectPredictive Modelling
dc.subjectMachine learning
dc.subjectBig data analytics
dc.subjectEnergy efficiency
dc.subjectPredictive modelling
dc.subjectEngineering(all)
dc.subjectBuilding and Construction
dc.titleComparison of Machine Learning Algorithms for Evaluating Building Energy Efficiency Using Big Data Analyticsen
dc.contributor.institutionCentre for Future Societies Research
dc.contributor.institutionHertfordshire Business School
dc.contributor.institutionUniversity of Hertfordshire
dc.contributor.institutionDepartment of Engineering and Technology
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionBioEngineering
dc.contributor.institutionCentre for Engineering Research
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85139070843&partnerID=8YFLogxK
rioxxterms.versionofrecord10.1108/jedt-05-2022-0238
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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