论文标题
使用统计模型通过监视VOC，CO $ _2 $和其他环境因素来检测建筑物中的占用
Using Statistical Models to Detect Occupancy in Buildings through Monitoring VOC, CO$_2$, and other Environmental Factors
论文作者
论文摘要
占用模式的动态模型已显示在优化建筑系统操作方面有效。先前的研究依赖于CO $ _2 $传感器和基于视觉的技术来确定占用模式。基于视觉的技术提供了高度准确的信息；但是，它们非常侵入性。因此，Motion或Co $ _2 $传感器在全球范围内被更广泛地采用。挥发性有机化合物（VOC）是源自乘员的另一种污染物。但是，有限的研究评估了乘员对VOC级别的影响。在本文中，在17,000平方英尺的开放式办公空间中记录了Co $ _2 $，VOC，光，温度和湿度的连续测量。使用不同的统计模型（例如，SVM，K-Nearest邻居和随机森林），我们评估了哪种环境因素的组合为占用者存在提供了更准确的见解。我们的初步结果表明，在某些情况下，VOC是占用检测的良好指标。还可以得出结论，适当的功能选择并开发适当的全球占用检测模型可以降低数据收集的成本和能量，而不会对准确性产生重大影响。
Dynamic models of occupancy patterns have shown to be effective in optimizing building-systems operations. Previous research has relied on CO$_2$ sensors and vision-based techniques to determine occupancy patterns. Vision-based techniques provide highly accurate information; however, they are very intrusive. Therefore, motion or CO$_2$ sensors are more widely adopted worldwide. Volatile Organic Compounds (VOCs) are another pollutant originating from the occupants. However, a limited number of studies have evaluated the impact of occupants on the VOC level. In this paper, continuous measurements of CO$_2$, VOC, light, temperature, and humidity were recorded in a 17,000 sqft open office space for around four months. Using different statistical models (e.g., SVM, K-Nearest Neighbors, and Random Forest) we evaluated which combination of environmental factors provides more accurate insights on occupant presence. Our preliminary results indicate that VOC is a good indicator of occupancy detection in some cases. It is also concluded that proper feature selection and developing appropriate global occupancy detection models can reduce the cost and energy of data collection without a significant impact on accuracy.
