Enhancing building energy efficiency estimations through graph machine learning: a focus on heating and cooling loads

Jabi, Wassim ORCID: https://orcid.org/0000-0002-2594-9568, Alymani, Abdulrahman and Alammar, Ammar 2025. Enhancing building energy efficiency estimations through graph machine learning: a focus on heating and cooling loads. Buildings 15 (18) , 3256. 10.3390/buildings15183256

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Abstract

In this paper, we introduce graph machine learning to enhance the estimation of heating and cooling loads in buildings, a critical factor in building energy efficiency. Traditional methods often overlook the complex interaction between building topology and geometric characteristics, leading to less accurate predictions. This research bridges this gap by incorporating these elements into a graph-based machine learning framework. This study introduces a parametric generative workflow to create a synthetic dataset, which is central to this research. This dataset encompasses multiple building forms, each with unique topological connections and attributes, ensuring a thorough analysis across varied building scenarios. The research involves simulating diverse building shapes and glazing scenarios with different window sizes and orientations. The study primarily utilizes Deep Graph Learning (DGL) for training, with Random Forest (RF) serving as a baseline for validation. Both DGL and RF algorithms demonstrate high performance in predicting heating and cooling loads.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Architecture
Subjects:	N Fine Arts > NA Architecture
Uncontrolled Keywords:	machine learning for energy analysis; graph machine learning; deep graph learning (DGL); building energy simulation (BES); heating and cooling loads
Publisher:	MDPI
ISSN:	0007-3725
Funders:	Research Funding Program (ORF-2025-1187), King Saud University, Riyadh, Saudi Arabia
Date of First Compliant Deposit:	9 September 2025
Date of Acceptance:	29 August 2025
Last Modified:	10 Sep 2025 09:45
URI:	https://orca.cardiff.ac.uk/id/eprint/181014

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