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A holistic passive design approach to optimize indoor environmental quality of a typical residential building in Hong Kong

机译:整体被动设计方法可优化香港典型住宅建筑的室内环境质量

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The green building assessment emphasizes the indoor environment quality (IEQ) by looking into the indoor air quality, lighting quality, acoustics, ventilation and thermal comfort conditions, which can be enhanced by effective initiatives at the early design stage. Designers and engineers usually consider exploiting passive designs to achieve a sustainable goal in building projects. In such background, this paper presents a holistic passive design approach by incorporating a robust sensitivity analysis to an efficient multi-objective optimization process to assess a typical high-rise residential building in hot and humid regions like Hong Kong. EnergyPlus and jEPlus are adopted to conduct modelling experiments with an input parametric matrix generated by the Latin Hypercube Sampling (LHS). All related indoor environment performance indices including the daylight, natural ventilation and thermal comfort are treated as optimization objectives and constraints to fulfil the local green building guidance. The non dominated sorting genetic algorithm (NSGA-II) is coupled with jEPlus to obtain the Pareto frontier by thoroughly searching the problem space constructed with screened out significant input variables from the sensitivity analysis. Furthermore, different post-optimization analysis methods are applied to decide the final optimum solution, where the total unmet time decreased by 11.2% in contrast with the baseline case. (C) 2016 Elsevier Ltd. All rights reserved.
机译:绿色建筑评估通过研究室内空气质量,照明质量,声学,通风和热舒适条件来强调室内环境质量(IEQ),可以在设计的早期阶段采取有效措施来增强室内质量。设计师和工程师通常考虑利用被动设计来实现建筑项目中的可持续目标。在这样的背景下,本文通过将鲁棒的灵敏度分析与有效的多目标优化过程相结合,提出了一种整体被动设计方法,以评估像香港这样炎热潮湿地区的典型高层住宅建筑。采用EnergyPlus和jEPlus对由Latin Hypercube Sampling(LHS)生成的输入参数矩阵进行建模实验。所有相关的室内环境性能指标(包括日光,自然通风和热舒适度)均被视为优化目标和约束条件,以符合当地的绿色建筑指南。非主导排序遗传算法(NSGA-II)与jEPlus结合使用,可以通过从敏感度分析中筛选出重要输入变量来彻底搜索构造的问题空间,从而获得帕累托边界。此外,采用不同的后优化分析方法来确定最终的最佳解决方案,与基线情况相比,总未满足时间减少了11.2%。 (C)2016 Elsevier Ltd.保留所有权利。

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