dc.contributor.author | Olu-Ajayi, Razak | |
dc.contributor.author | Alaka, Hafiz | |
dc.contributor.author | Sulaimon, Ismail | |
dc.contributor.author | Sunmola, Funlade | |
dc.contributor.author | Ajayi, Saheed | |
dc.date.accessioned | 2022-10-06T14:45:01Z | |
dc.date.available | 2022-10-06T14:45:01Z | |
dc.date.issued | 2022-02-28 | |
dc.identifier.citation | Olu-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.other | ORCID: /0000-0003-0326-1719/work/159834892 | |
dc.identifier.uri | http://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.abstract | The 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.extent | 14 | |
dc.format.extent | 900258 | |
dc.language.iso | eng | |
dc.relation.ispartof | Energy for Sustainable Development | |
dc.subject | Building design stage | |
dc.subject | Building designers | |
dc.subject | Building energy performance | |
dc.subject | Energy efficiency | |
dc.subject | Energy rating | |
dc.subject | Machine learning algorithms | |
dc.subject | Geography, Planning and Development | |
dc.subject | Renewable Energy, Sustainability and the Environment | |
dc.subject | Management, Monitoring, Policy and Law | |
dc.title | Machine learning for energy performance prediction at the design stage of buildings | en |
dc.contributor.institution | Hertfordshire Business School | |
dc.contributor.institution | Centre for Climate Change Research (C3R) | |
dc.contributor.institution | Centre for Future Societies Research | |
dc.contributor.institution | Department of Engineering and Technology | |
dc.contributor.institution | School of Physics, Engineering & Computer Science | |
dc.contributor.institution | Materials and Structures | |
dc.contributor.institution | Centre for Engineering Research | |
dc.description.status | Peer reviewed | |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85119170504&partnerID=8YFLogxK | |
rioxxterms.versionofrecord | 10.1016/j.esd.2021.11.002 | |
rioxxterms.type | Journal Article/Review | |
herts.preservation.rarelyaccessed | true | |