Prediction of Energy Efficiency for Residential Buildings Using Supervised Machine Learning Algorithms - Article

Aug 3, 2026

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Digitalization and the availability of large datasets enable machine learning methods to improve building energy-efficiency prediction. Research in Saudi Arabia’s building sector has often relied on simulation or modeling rather than measured, data-driven evidence, and simulation outputs can diverge from real energy performance. This study predicts residential energy efficiency in KSA using supervised machine learning based on energy-audit data from 200 homes. Five algorithms are evaluated, and elastic net regression delivers the best prediction of energy consumption while identifying key explanatory variables.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Introduction

Background and motivation

Building energy use and sustainability context

Study aim and approach

Methodology

Data source: energy audit of 200 homes

Supervised machine learning models used

Explanatory variable analysis

energies Article Prediction of Energy Efficiency for Residential Buildings Using Supervised Machine Learning Algorithms Tahir Mahmood 1 and Muhammad Asif 2,3, * Citation: Mahmood, T.; Asif, M. Prediction of Energy Efficiency for Residential Buildings Using Supervised Machine Learning Algorithms. Energies 2024, 17, 4965 . [https://doi.org/10.3390/en17194965](https://doi.org/10.3390/en17194965) Academic Editors: Gerardo Maria Mauro and Christian Inard Received: 26 July 2024 Revised: 16 September 2024 Accepted: 25 September 2024 Published: 4 October 2024 Copyright: © 2024 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK 2 Architectural Engineering and Construction Management, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia 3 IRC Sustainable Energy Systems, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia * Correspondence: [dr.m.asif@gmail.com](dr.m.asif@gmail.com) Abstract: In the era of digitalization, the large availability of data and innovations in machine learning algorithms provide new potential to improve the prediction of energy efficiency in buildings. The building sector research in the Kingdom of Saudi Arabia (KSA) lacks actual/measured data-based studies as the existing studies are predominantly modeling-based. The results of simulation-based studies can deviate from the actual energy performance of buildings due to several factors. A clearer understanding of building energy performance can be better established through actual data-based analysis. This study aims to predict the energy efficiency of residential buildings in the KSA using supervised machine learning algorithms. It analyzes residential energy trends through data collected from an energy audit of 200 homes. It predicts energy efficiency using five supervised machine learning algorithms: ridge regression, least absolute shrinkage and selection operator (LASSO) regression, a least angle regression (LARS) model, a Lasso-LARS model, and an elastic net regression (ENR) model. It also explores the most significant explanatory energy efficiency variables. The results reveal that the ENR model outperforms other models in predicting energy consumption. This study offers a new and prolific avenue for the research community and other building sector stakeholders, especially regulators and policymakers. Keywords: buildings; energy efficiency; machine learning; renewable energy; energy management 1. Introduction Global warming is considered to be the most daunting challenge to the planet [1] . Wide-ranging weather anomalies and a pattern of more frequent and severe natural catastrophes, such as storms, flooding, droughts, and forest fires, are being brought on by global warming and the ensuing changes in the climate. To combat climate change and achieve sustainability goals, the global community requires major changes across all spheres of life. The building sector is an essential and critical part of modern societies. Buildings not only influence the infrastructure but also the socio-economic and technological fabric of society. The building sector, accounting for 36% and 40% of the total energy and natural resource consumption, respectively, while emitting over one-third of greenhouse gas (GHG) emissions, is a significant stakeholder in the global energy and environmental scenarios [2] . Buildings’ role in energy and environmental outlooks worldwide is projected to be more significant in the future owing to factors like growing population and urbanization. Compared to 2018, by 2050,

FAQs

Why does the study emphasize measured data over simulation-based research?

Simulation-based studies may deviate from actual building energy performance due to multiple influencing factors. Measured, data-driven analysis provides a clearer understanding of real energy efficiency trends.

Which supervised machine learning algorithms are compared in predicting residential energy efficiency?

The study evaluates ridge regression, LASSO regression, LARS, Lasso-LARS, and elastic net regression (ENR).

What is the main finding about model performance?

Elastic net regression (ENR) outperforms the other models in predicting energy consumption, and it is used to explore the most significant explanatory variables.

Aug 3, 2026

Upload by Maeve