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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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/jedt-05-2022-0238","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T05:58:16Z","timestamp":1663912696000},"page":"1325-1350","source":"Crossref","is-referenced-by-count":18,"title":["Comparison of machine learning algorithms for evaluating building energy efficiency using big data analytics"],"prefix":"10.1108","volume":"22","author":[{"given":"Christian Nnaemeka","family":"Egwim","sequence":"first","affiliation":[]},{"given":"Hafiz","family":"Alaka","sequence":"additional","affiliation":[]},{"given":"Oluwapelumi Oluwaseun","family":"Egunjobi","sequence":"additional","affiliation":[]},{"given":"Alvaro","family":"Gomes","sequence":"additional","affiliation":[]},{"given":"Iosif","family":"Mporas","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2022,9,26]]},"reference":[{"key":"key2024061307235826200_ref001","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2021.100077","article-title":"Deep learning for estimating energy savings of early-stage facade design decisions","volume":"5","year":"2021","journal-title":"Energy and AI"},{"key":"key2024061307235826200_ref002","unstructured":"Affairs Committee, R. 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