Predicting energy consumption of building clusters at the design stage using machine learning models

Abdulhameed Babatunde Owolabi; Abdullahi Yahaya; Mohammad Amir; Abdulfatai Olatunji Yakub; Miroslava Kavgic; Dongjun Suh

doi:10.1016/j.asej.2025.103481

Back

Predicting energy consumption of building clusters at the design stage using machine learning models

Journal article

Open access

Peer reviewed

Predicting energy consumption of building clusters at the design stage using machine learning models

Abdulhameed Babatunde Owolabi, Abdullahi Yahaya, Mohammad Amir, Abdulfatai Olatunji Yakub, Miroslava Kavgic and Dongjun Suh

Ain Shams Engineering Journal, v 16(8), 103481

Aug 2025

DOI: https://doi.org/10.1016/j.asej.2025.103481

Featured in Collection : UN Sustainable Development Goals @ Drexel

Files and links (1)

url

https://doi.org/10.1016/j.asej.2025.103481View

Published, Version of Record (VoR) Open

Abstract

Building clusters

Building design stage

COVID-19

Energy Conservation

Energy Consumption

Machine Learning

The environmental impact of high energy consumption in buildings during the COVID-19 pandemic has led to the adopting of data-driven approaches for enhanced decision-making and energy savings. However, forecasting energy use during the early design phase remains limited. This study investigates how building clusters affect model performance at the design stage using five machine-learning techniques with a dataset of 10,264 buildings. Model performances were evaluated using their accuracy, RMSE, MAE, MSE, and R2 metrics. Results showed that DNN achieved the best accuracy score of 98%, followed by MLPNN and SVMW with accuracy scores of 95% and 92%, respectively. The study proposes a general framework to predict average annual energy use across different building types at the early design stage, supporting informed and sustainable architectural decisions.

Metrics

1 Record Views

3 citations in Web of Science

Details

Title: Predicting energy consumption of building clusters at the design stage using machine learning models
Creators: Abdulhameed Babatunde Owolabi - Kyungpook National University
Abdullahi Yahaya - Kyungpook National University
Mohammad Amir - University of Liverpool
Abdulfatai Olatunji Yakub - Kyungpook National University
Miroslava Kavgic - University of Ottawa
Dongjun Suh - Kyungpook National University
Publication Details: Ain Shams Engineering Journal, v 16(8), 103481
Publisher: Elsevier
Resource Type: Journal article
Language: English
Academic Unit: Civil, Architectural, and Environmental Engineering
Web of Science ID: WOS:001502099100004
Scopus ID: 2-s2.0-105005592175
Other Identifier: 991022197408104721