研究当前和未来气候条件下城市微气候对建筑热工性能的影响——以埃及新奥布尔市为例

Eman A. Saleh, Bassel Essam
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引用次数: 0

摘要

人工智能(AI)使城市规划者能够预测小气候条件,使他们能够聪明地思考并优化城市设计。由于室内模拟方法不考虑小气候相互作用,因此使用耦合的室外-室内模拟来克服软件包无法在一个工具中处理两个目标的问题。为了预测气象气候条件和二氧化碳(CO2),使用DesignBuilder V4.2将envi -满足V4.0模拟(考虑室外条件)与室内模拟相结合。本文基于埃及新奥布尔市的一个案例研究,首先使用带有几个天气文件(EPW、STAT、DDY和Audit)的设计规范来创建社区,以模拟和估计未来的变化,然后比较2080年的当前和未来。研究结果强调了人工智能在建筑优化、适应和长期可持续设计预测方面的重要性。此外,该研究还提出了可以解决的设计中的各种缺陷,以适应气候变化。通过对2023年和2080年两个代表性设计日的主动模拟,在选定的社区中解决了这一问题,从而产生影响热舒适的某些参数,包括空气温度、相对湿度、风速、二氧化碳、预测平均投票(PMV)和生理等效温度(PET)。本研究的结果可作为建筑师、城市设计师和规划师在早期设计阶段实现可持续住区的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigating the Impacts of Urban Microclimate on Building Thermal Performance in Present and Future Climatic Conditions: A Case of New Obour City, Egypt
Artificial intelligence (AI) has enabled urban planners to forecast microclimate conditions, allowing them to think smartly and optimise urban design. Coupled outdoor-indoor simulations were used to overcome the packages’ inability to handle both objectives in one tool because indoor simulation methods do not consider microclimatic interactions. To predict metrological climate conditions and carbon dioxide (CO2), ENVI-met V4.0 simulations (accounting for outdoor conditions) were combined with indoor simulations using DesignBuilder V4.2. This paper is based on a case study in New Obour City, Egypt, that begins by creating the neighbourhood using design specifications with several weather files (EPW, STAT, DDY, and Audit) to simulate and estimate future changes, then comparing the current and future in 2080. The findings highlight the significance of AI in architectural optimisation, adaptation, and prediction for long-term sustainable design. Moreover, the study presents the various flaws in the design that might be addressed to accommodate climate change. That was tackled by the active simulation for the two representative design days in the years 2023 and 2080 in the chosen neighbourhood to result in certain parameters influencing thermal comfort, including air temperature, relative humidity, wind speed, CO2, predicted mean vote (PMV), and physiological equivalent temperature (PET). The results of this study can serve as a reference for architects, urban designers, and planners in the early design stages to attain sustainable residential neighbourhoods.
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