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Approximation of simulation-derived visual comfort indicators in office spaces: a comparative study in machine learning

机译：模拟派生的办公室视觉舒适度指标的逼近：机器学习的比较研究

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摘要

In performance-oriented architectural design, the use of advanced computational simulation tools may provide valuable insight during design. However, the use of such tools is often a bottleneck in the design process, given that computational requirements are usually high. This is a fact that mostly affects the early conceptual stage of design, where crucial decisions mainly occur, and available time is limited. In order to deal with this, decision-makers frequently resort to drawing conclusions from experience, and, as such, valuable insight that advanced computational methods have to offer is lost. This paper explores an alternative approach, which builds on machine-learning algorithms that inductively learn from simulation-derived data, yielding models that approximate to a good degree and are orders of magnitude faster. We focus on visual comfort of office spaces. This is a type of space that specifically requires visual comfort more than others. Three machine-learning methods are compared with respect to applicability in approximating daylight autonomy and daylight glare probability. The comparison focuses on accuracy and time cost of training and estimation. Results demonstrate that machine-learning-based approaches achieve a favourable trade-off between accuracy and computational cost, and provide a worthwhile alternative for performance evaluations during architectural conceptual design.
机译：在面向性能的体系结构设计中，使用高级计算仿真工具可能会在设计过程中提供有价值的见解。但是，鉴于计算需求通常很高，因此使用此类工具通常是设计过程中的瓶颈。这是一个主要影响设计早期概念阶段的事实，该阶段主要发生关键决策，可用时间有限。为了解决这个问题，决策者经常求助于经验，因此失去了高级计算方法必须提供的宝贵见解。本文探索了一种替代方法，该方法基于机器学习算法，该算法从模拟派生的数据中归纳学习，从而得出近似良好且速度快几个数量级的模型。我们专注于办公空间的视觉舒适度。这种空间比其他空间更需要视觉舒适。比较了三种机器学习方法在近似日光自治度和日光眩光概率方面的适用性。比较的重点是训练和估计的准确性和时间成本。结果表明，基于机器学习的方法在精度和计算成本之间实现了良好的折衷，并为架构概念设计期间的性能评估提供了有价值的替代方案。

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