Optimization and Prediction of Different Building Forms for Thermal Energy Performance in the Hot Climate of Cairo Using Genetic Algorithm and Machine Learning

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Computation Pub Date : 2023-10-02 DOI:10.3390/computation11100192
Amany Khalil, Anas M. Hosney Lila, Nouran Ashraf
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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.
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基于遗传算法和机器学习的开罗炎热气候下不同建筑形式热能性能优化与预测
气候变化危机导致在建筑设计中需要使用可持续的方法,包括建筑形式和朝向的决定,这可以节省建筑消耗的大量能源。以前的一些研究已经优化了建筑形式和围护结构的能源性能,但各种可能的建筑形式对特定气候的孤立影响尚未得到充分调查。本文提出了四种新的办公楼形态生成方法(五边形和十角形之间变化的多边形;像素是复杂的立方形式;字母包括H、L、U、T;交叉和复杂的立方体;以及圆形家庭(包括圆形和椭圆形),并评估其在开罗(炎热气候)的年度热能利用强度(EUI)。结果表明,与基本情况相比,所提出的方法在提高新形式的能源性能方面具有适用性。将优化的结果放在一起进行比较,并根据其不同的体系结构方面和性能讨论这四个系列。为圆族(最高性能)开发了散点图,以测试每个动态参数对EUI的影响。与其他族相比,圆族优化过程的计算时间明显较高。为此,在模拟了1726次迭代后,建立了圆形族的人工神经网络(ANN)预测模型。1200个配置的训练用于预测剩余526个迭代的年度EUI。将人工神经网络预测值与训练值进行比较,以确定节省的时间和准确性。
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来源期刊
Computation
Computation Mathematics-Applied Mathematics
CiteScore
3.50
自引率
4.50%
发文量
201
审稿时长
8 weeks
期刊介绍: Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.
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