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Optimization and prediction of different building forms for thermal energy performance in the hot climate of Cairo using genetic algorithm and machine learning

Khalil, Amany; Lila, Anas M. Hosney; Ashraf, Nouran

Optimization and prediction of different building forms for thermal energy performance in the hot climate of Cairo using genetic algorithm and machine learning Thumbnail


Authors

Amany Khalil

Anas M. Hosney Lila

Nouran Ashraf



Abstract

The climate change crisis has resulted in the need to use sustainable methods in architectural design, including building form and orientation decisions that can save a significant amount of energy consumed by a building. Several previous studies have optimized building form and envelope for energy performance, but the isolated effect of varieties of possible architectural forms for a specific climate has not been fully investigated. This paper proposes four novel office building form generation methods (the polygon that varies between pentagon and decagon; the pixels that are complex cubic forms; the letters including H, L, U, T; cross and complex cubic forms; and the round family including circular and oval forms) and evaluates their annual thermal energy use intensity (EUI) for Cairo (hot climate). Results demonstrated the applicability of the proposed methods in enhancing the energy performance of the new forms in comparison to the base case. The results of the optimizations are compared together, and the four families are discussed in reference to their different architectural aspects and performance. Scatterplots are developed for the round family (highest performance) to test the impact of each dynamic parameter on EUI. The round family optimization process takes a noticeably high calculation time in comparison to other families. Therefore, an Artificial Neural Network (ANN) prediction model is developed for the round family after simulating 1726 iterations. Training of 1200 configurations is used to predict annual EUI for the remaining 526 iterations. The ANN predicted values are compared against the trained to determine the time saved and accuracy.

Journal Article Type Article
Acceptance Date Jun 28, 2023
Online Publication Date Oct 2, 2023
Publication Date Oct 2, 2023
Deposit Date Mar 19, 2024
Publicly Available Date Mar 20, 2024
Journal Computation
Electronic ISSN 2079-3197
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 11
Issue 10
Article Number 192
DOI https://doi.org/10.3390/computation11100192
Keywords Applied Mathematics, Modeling and Simulation, General Computer Science, Theoretical Computer Science
Public URL https://uwe-repository.worktribe.com/output/11172805

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