Supporting early-stage design decisions with building performance optimisation: Findings from a design experiment

实验设计 计算机科学 高效能源利用 空格(标点符号) 建筑工程 工程类 数学 统计 电气工程 操作系统
作者
Shuai Lu,Yiying Luo,Wen Gao,Borong Lin
出处
期刊:Journal of building engineering [Elsevier]
卷期号:82: 108298-108298
标识
DOI:10.1016/j.jobe.2023.108298
摘要

Building performance optimisation (BPO) has been intensively explored due to its potential in improving building performance and design efficiency. However, studies on its application in supporting early-stage design decisions are rather limited, causing its actual effectiveness to remain questionable. This paper addresses the problem through a design experiment, where participants designed an office building for energy efficiency, using first the conventional method and then BPO. They also evaluated others' design outcomes and completed a questionnaire about the experiment afterwards. The results showed that BPO is effective in supporting early-stage architectural design even for novices. It turned manual designs that are often averagely energy-efficient in the solution space into top designs without significantly affecting other design merits. The average energy use intensity (EUI) decreased by 8.5 % in this experiment. However, it showed a narrow advantage (0.5 % on average) over evaluating a series of design samples and no clear superiority over less-automated supporting methods with more involvement of designers, as its effectiveness is limited by technical difficulties in implementing certain variables strongly correlated with energy efficiency. In addition, while the majority of participants acknowledge the effectiveness of BPO and the acceptance of its design outcomes, manual designs were still preferred by more, mostly for better aesthetics and better alignment of design concepts, implying the necessity of designer's interventions before using BPO outcomes in design practice. The useability of BPO also needs to be improved by shortening the calculation time and lowering the difficulties in building desirable parametric models.
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