A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings

Aug 4, 2026

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Improving energy efficiency is a major concern in residential buildings, balancing economic prosperity with environmental stability. Limited research has systematically pinpointed the dominant factors affecting residential energy efficiency at scale, leaving a significant gap. This work uses a large-scale energy performance certificate dataset, applying dimensionality reduction and feature selection to identify key predictors. Results highlight CO2 emissions per floor area, current energy use, heating cost current, and current CO2 emissions, with additional influences from floor area, lighting cost, and heated rooms. Machine learning models including Random Forest, Gradient Boosting, XGBoost, and LightGBM show low mean square error, and a customised interface supports data visualisation and evaluation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages35

Reading Time1 h 28 min

AccessFree to download

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Citation: Shakeel, HM and Iram, S and Hill, R and Athar Farid, HM and Sheikh-Akbari, A and Saleem, F (2025) A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings. Buildings, 15 (8) . pp. 1-34. ISSN 2075-5309 DOI: [https://doi.org/10.3390/buildings15081275](https://doi.org/10.3390/buildings15081275) Link to Leeds Beckett Repository record: [https://eprints.leedsbeckett.ac.uk/id/eprint/12051/](https://eprints.leedsbeckett.ac.uk/id/eprint/12051/) Document Version: Article (Published Version) Creative Commons: Attribution 4.0 © 2025 by the authors The aim of the Leeds Beckett Repository is to provide open access to our research, as required by funder policies and permitted by publishers and copyright law. The Leeds Beckett repository holds a wide range of publications, each of which has been checked for copyright and the relevant embargo period has been applied by the Research Services team. We operate on a standard take-down policy. If you are the author or publisher of an output and you would like it removed from the repository, please contact us and we will investigate on a case-by-case basis. Each thesis in the repository has been cleared where necessary by the author for third party copyright. If you would like a thesis to be removed from the repository or believe there is an issue with copyright, please contact us on [openaccess@leedsbeckett.ac.uk](openaccess@leedsbeckett.ac.uk) and we will investigate on a case-by-case basis. Article A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings Hafiz Muhammad Shakeel 1, *, Shamaila Iram 1, Richard Hill 1, Hafiz Muhammad Athar Farid 1, Akbar Sheikh-Akbari 2, * and Farrukh Saleem 2 Academic Editor: Apple L.S. Chan Received: 26 February 2025 Revised: 3 April 2025 Accepted: 7 April 2025 Published: 13 April 2025 Citation: Shakeel, H.M.; Iram, S.; Hill, R.; Athar Farid, H.M.; Sheikh-Akbari, A.; Saleem, F. A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings. Buildings 2025, 15, 1275 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)buildings15081275 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . 1 Department of Computer Science, University of Huddersfield, Huddersfield HD1 3DH, UK; [s.iram@hud.ac.uk](s.iram@hud.ac.uk) (S.I.); [r.hill@hud.ac.uk](r.hill@hud.ac.uk) (R.H.); [hafiz.farid@hud.ac.uk](hafiz.farid@hud.ac.uk) (H.M.A.F.) 2 School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS6 3QR, UK; [f.saleem@leedsbeckett.ac.uk](f.saleem@leedsbeckett.ac.uk) * Correspondence: hafiz.shakeel@hud.ac.uk (H.M.S.); a.sheikh-akbari@leedsbeckett.ac.uk (A.S.-A.) Abstract: Improving energy efficiency is a major concern in residential buildings for economic prosperity and environmental stability. Despite growing interest in this area, limited research has been conducted to systematically identify the primary factors that influence residential energy efficiency at scale, leaving a significant research gap. This paper addresses the gap by exploring the key determinant factors of energy efficiency in residential properties using a large-scale energy performance certificate dataset. Dimensionality reduction and feature selection techniques were used to pinpoint the key predictors of energy efficiency. The consistent results emphasise the importance of CO 2 emissions per floor area, current energy consumption, heating cost current, and CO 2 emissions current as primary determinants, alongside factors such as tot

FAQs

What problem does the paper address regarding residential energy efficiency?

It targets the lack of systematic research identifying the primary factors that influence residential energy efficiency at scale.

Which data and methods are used to find key predictors?

The study uses a large-scale energy performance certificate dataset and applies dimensionality reduction and feature selection.

Which machine learning models provide the best prediction performance?

Random Forest, Gradient Boosting, XGBoost, and LightGBM deliver the lowest mean square error scores, demonstrating effective forecasting of energy performance.

Aug 4, 2026

Upload by Miles