通过入住后评估和多目标优化解决舒适性改造难题

IF 1.5 4区 工程技术 Q3 CONSTRUCTION & BUILDING TECHNOLOGY
Chuan-Rui Yu, Xuan Liu, Qian-Cheng Wang, Dujuan Yang
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引用次数: 3

摘要

制定适当的建筑改造策略是一项具有挑战性的任务。本案例研究提出了一种多标准决策支持方法,该方法在建筑改造的早期探索阶段提出了具有一系列多样性的最佳解决方案和替代设计参考。该方法采用实用的两步方法来识别关键的舒适性和能源问题,并通过基于遗传算法的多目标优化生成优化设计选项。第一步是基于入住后评估,该评估交叉参考基准和相关性,并将它们与非线性满意度理论相结合,以提取关键的舒适因素。第二步将以前的输出参数化,作为进行建筑模拟实践的目标。案例研究是伦敦一个典型的战后高度玻璃化的开放式办公室。入住后评估结果将阳光直射眩光、室内温度和其他入住者的噪音确定为关键的舒适因素。模拟和优化通过分析480个生成的Pareto前沿来提取最佳改造策略。所提出的方法为改造解决方案提供了基于标准的滤波方法,并考虑了能量和舒适度目标之间的权衡。该方法可以转化为设计支持工具,以确定建筑环境优化的关键舒适因素,并在建筑改造中创造可持续性。实际应用:本研究表明,统计分析可以与参数设计工具和多目标优化相结合。它直接将用户的主观意见与最终的设计方案联系起来,为数据驱动的生成设计提供了一种新的方法。作为一个定量过程,所提出的框架可以通过程序实现自动化,减少优化过程中的人力投入,并减少设计问题定义和分析过程中对人力经验的依赖。它还可以避免人为错误,例如忽略一些关键因素。在多目标优化过程中,生成了大量的设计选项,其中许多选项在Pareto前沿进行了优化。与设计全新的选项相比,探索这些选项可能是一个不那么耗费人力的过程,尤其是在早期的设计探索阶段。总的来说,这可能是未来生成设计研究的一个潜在方向,这将大大减少可持续设计对高建筑性能的技术障碍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solving the comfort-retrofit conundrum through post-occupancy evaluation and multi-objective optimisation
Developing appropriate building retrofit strategies is a challenging task. This case study presents a multi-criteria decision-supporting method that suggests optimal solutions and alternative design references with a range of diversity at the early exploration stage in building retrofit. This method employs a practical two-step method to identify critical comfort and energy issues and generate optimised design options with multi-objective optimisation based on a genetic algorithm. The first step is based on a post-occupancy evaluation, which cross-refers benchmarking and correlation and integrates them with non-linear satisfaction theory to extract critical comfort factors. The second step parameterises previous outputs as objectives to conduct building simulation practice. The case study is a typical post-war highly glazed open-plan office in London. The post-occupancy evaluation result identifies direct sunlight glare, indoor temperature, and noise from other occupants as critical comfort factors. The simulation and optimisation extract the optimal retrofit strategies by analysing 480 generated Pareto fronts. The proposed method provides retrofit solutions with a criteria-based filtering method and considers the trade-off between the energy and comfort objectives. The method can be transformed into a design-supporting tool to identify the key comfort factors for built environment optimisation and create sustainability in building retrofit. Practical application : This study suggested that statistical analysis could be integrated with parametric design tools and multi-objective optimisation. It directly links users’ subjective opinions to the final design solutions, suggesting a new method for data-driven generative design. As a quantitative process, the proposed framework could be automated with a program, reducing the human effort in the optimisation process and reducing the reliance on human experience in the design question defining and analysis process. It might also avoid human mistakes, e.g. overlooking some critical factors. During the multi-objective optimisation process, large numbers of design options are generated, and many of them are optimised at the Pareto front. Exploring these options could be a less human effort-intensive process than designing completely new options, especially in the early design exploration phase. Overall, this might be a potential direction for future study in generative design, which greatly reduce the technical obstacle of sustainable design for high building performance.
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来源期刊
Building Services Engineering Research & Technology
Building Services Engineering Research & Technology 工程技术-结构与建筑技术
CiteScore
4.30
自引率
5.90%
发文量
38
审稿时长
>12 weeks
期刊介绍: Building Services Engineering Research & Technology is one of the foremost, international peer reviewed journals that publishes the highest quality original research relevant to today’s Built Environment. Published in conjunction with CIBSE, this impressive journal reports on the latest research providing you with an invaluable guide to recent developments in the field.
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