论文标题
使用数字双胞胎定位乘员反馈：自适应时空热偏好采样以优化个人舒适模型
Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models
论文作者
论文摘要
从建筑物居民那里收集密集的纵向热偏好数据正在成为一种创新的手段，以表征建筑物的性能和使用它们的人的性能。这些技术的乘员在几天或几周内经常使用智能手机或智能手表提供主观反馈。目的是将数据以较高的空间和时间多样性收集，以最好地表征建筑物和乘员的偏好。但实际上，让乘员以临时或固定的间隔方式做出反应会产生不需要的调查疲劳和冗余数据。本文概述了一种基于方案的（虚拟实验）方法，该方法用于使用智能手表优化数据采样，以在个人热偏好模型中获得可比的精度，并具有较少的数据。该方法使用BIM提取的空间数据和基于图形神经网络（GNN）的建模来查找类似舒适性偏好的区域，以确定触发乘员提供反馈的最佳场景。将此方法与两个基线分区和来自两个基于字段的数据集的通用4x4平方米网格方法进行比较。结果表明，所提出的build2Vec方法比基于空格和基于方格的采样方法的总体采样质量高18-23 \％。 build2Vec方法在删除冗余乘员反馈点时的性能也类似于基线，但具有更好的可扩展性潜力。
Collecting intensive longitudinal thermal preference data from building occupants is emerging as an innovative means of characterizing the performance of buildings and the people who use them. These techniques have occupants giving subjective feedback using smartphones or smartwatches frequently over the course of days or weeks. The intention is that the data will be collected with high spatial and temporal diversity to best characterize a building and the occupant's preferences. But in reality, leaving the occupant to respond in an ad-hoc or fixed interval way creates unneeded survey fatigue and redundant data. This paper outlines a scenario-based (virtual experiment) method for optimizing data sampling using a smartwatch to achieve comparable accuracy in a personal thermal preference model with fewer data. This method uses BIM-extracted spatial data and Graph Neural Network-based (GNN) modeling to find regions of similar comfort preference to identify the best scenarios for triggering the occupant to give feedback. This method is compared to two baseline scenarios that use conventional zoning and a generic 4x4 square meter grid method from two field-based data sets. The results show that the proposed Build2Vec method has an 18-23\% higher overall sampling quality than the spaces-based and square-grid-based sampling methods. The Build2Vec method also performs similar to the baselines when removing redundant occupant feedback points but with better scalability potential.
