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dc.contributor.authorOlu-Ajayi, Razak
dc.contributor.authorAlaka, Hafiz
dc.contributor.authorSulaimon, Ismail
dc.contributor.authorSunmola, Funlade
dc.contributor.authorAjayi, Saheed
dc.date.accessioned2022-10-06T14:45:01Z
dc.date.available2022-10-06T14:45:01Z
dc.date.issued2022-02-28
dc.identifier.citationOlu-Ajayi , R , Alaka , H , Sulaimon , I , Sunmola , F & Ajayi , S 2022 , ' Machine learning for energy performance prediction at the design stage of buildings ' , Energy for Sustainable Development , vol. 66 , pp. 12-25 . https://doi.org/10.1016/j.esd.2021.11.002
dc.identifier.urihttp://hdl.handle.net/2299/25785
dc.description© 2021 International Energy Initiative. Published by Elsevier Inc. All rights reserved. This is the author’s accepted version of the work, which was originally published at https://doi.org/10.1016/j.esd.2021.11.002. The author’s accepted manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.
dc.description.abstractThe substantial amount of energy consumption in buildings and the associated adverse effects prompts the importance of understanding building energy efficiency. Developing an energy prediction model with high accuracy is considered one of the most effective approach to understanding building energy efficiency. Therefore, various studies have developed diverse models for predicting building energy consumption focused on the current building stock. However, to ensure future buildings are constructed to be more energy efficient, it is essential to consider energy efficiency at the design stage. Machine Learning (ML) algorithms are considered the most contemporary and best method for prediction, and these algorithms (such as Support Vector Machine (SVM) and Decision Tree (DT), among others) have gained much attention in the field of energy prediction. However, no study has explored the application of hyper parameter tuning and feature selection methods in developing a design stage Machine Learning (ML) energy predictive model. In this research, nine machine learning classification-based algorithms were compared for energy performance assessment at the design stage of residential buildings. Additionally, feature selection and hyper parameter tunning were implemented. The result shows that it is possible to develop a high performing ML model for building energy use prediction at the design stage. Furthermore, Gradient Boosting (GB) outperformed the other models with an accuracy of 0.67 for predicting building energy performance.en
dc.format.extent14
dc.format.extent900258
dc.language.isoeng
dc.relation.ispartofEnergy for Sustainable Development
dc.subjectBuilding design stage
dc.subjectBuilding designers
dc.subjectBuilding energy performance
dc.subjectEnergy efficiency
dc.subjectEnergy rating
dc.subjectMachine learning algorithms
dc.subjectGeography, Planning and Development
dc.subjectRenewable Energy, Sustainability and the Environment
dc.subjectManagement, Monitoring, Policy and Law
dc.titleMachine learning for energy performance prediction at the design stage of buildingsen
dc.contributor.institutionCentre for Climate Change Research (C3R)
dc.contributor.institutionCentre for Future Societies Research
dc.contributor.institutionHertfordshire Business School
dc.contributor.institutionDepartment of Engineering and Technology
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionMaterials and Structures
dc.contributor.institutionCentre for Engineering Research
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85119170504&partnerID=8YFLogxK
rioxxterms.versionofrecord10.1016/j.esd.2021.11.002
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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