Alharbi, E A and Alsehaimi, A (2025) Machine learning model to predict the cooling load of mosque buildings during the design stage. International Journal of Construction Management, 25(8), pp. 965-979. ISSN 1562-3599
Abstract
The building sector accounts for almost 40% of the total global energy consumption. Mosques are among these buildings which found to consume huge amount of energy. There is an increased demand to establish new sustainable mosques. Although the design stage proves the superiority to reduce energy consumption up to 70%, majority of previous studies were found at operation and maintenance stages. This is due to the complexity of this stage, lack of information and support tools to assist designers. The current design tools and prediction models to estimate cooling load are complex, time-consuming and fail to meet the requirement of designers especially at the design stage. This paper aims to develop a machine learning prediction model to assist mosque designers in providing a range of lowest cooling load design alternatives. In developing the model, artificial neural network was applied including back-propagation strategy and Levenberg-Marquardt algorithm. The least mean square error was at 6.27 × (Formula presented.) and the accuracy of the model reached at 99.88%. Further, nine experts were participated to estimate the effectiveness and ease of use of the prediction model. The model proved to be rapid, accurate and can be used by designers with no many simulation scenarios or complex calculations.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial neural network; cooling load estimation; energy efficiency design; machine learning; sustainable mosques |
| Index terms: | operation and maintenance, strategy, machine learning, energy consumption, estimation, effectiveness, mosque, estimate, accuracy, design stage, complexity, energy efficiency, propagation, designer, prediction model, mean square error, artificial neural network |
| Subjects: | sustainability and energy, architectural elements, performance management, prediction and forecasting, professional practice, management, energy systems, modelling and simulation, probability and distributions, financial and cost management, maintenance engineering, professional development, engineering process, artificial intelligence, systems engineering, profession |
| Topics: | Sustainability, Quality Management, Digital Applications, Engineering Principles, Roles and Professions, Information Management, Cost Management, Business Strategy, Research Practice, Design Practice |
| Descriptive scope: | 3 PCA |
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