[论文解读] Machine Learning-Based Automated Thermal Comfort Prediction: Integration of Low-Cost Thermal and Visual Cameras for Higher Accuracy

Roshanak Ashrafi, Mona Azarbayjani+1 more|arXiv (Cornell University)|Apr 14, 2022

Building Energy and Comfort Optimization被引用 5

一句话总结

本研究提出一种基于机器学习的系统，通过融合低成本热成像与可见光摄像头，实现实时个性化热舒适度预测。通过将FLIR Lepton热成像摄像头获取的面部温度数据与可见光图像相结合，并与可穿戴IButton传感器进行验证，即使在使用非辐射校准的热成像数据时，只要训练数据集足够大，该系统仍能利用随机森林和K近邻模型实现高精度预测。

ABSTRACT

Recent research is trying to leverage occupants' demand in the building's control loop to consider individuals' well-being and the buildings' energy savings. To that end, a real-time feedback system is needed to provide data about occupants' comfort conditions that can be used to control the building's heating, cooling, and air conditioning (HVAC) system. The emergence of thermal imaging techniques provides an excellent opportunity for contactless data gathering with no interruption in occupant conditions and activities. There is increasing attention to infrared thermal camera usage in public buildings because of their non-invasive quality in reading the human skin temperature. However, the state-of-the-art methods need additional modifications to become more reliable. To capitalize potentials and address some existing limitations, new solutions are required to bring a more holistic view toward non-intrusive thermal scanning by leveraging the benefit of machine learning and image processing. This research implements an automated approach to collect and register simultaneous thermal and visual images and read the facial temperature in different regions. This paper also presents two additional investigations. First, through utilizing IButton wearable thermal sensors on the forehead area, we investigate the reliability of an in-expensive thermal camera (FLIR Lepton) in reading the skin temperature. Second, by studying the false-color version of thermal images, we look into the possibility of non-radiometric thermal images for predicting personalized thermal comfort. The results shows the strong performance of Random Forest and K-Nearest Neighbor prediction algorithms in predicting personalized thermal comfort. In addition, we have found that non-radiometric images can also indicate thermal comfort when the algorithm is trained with larger amounts of data.

研究动机与目标

• 开发一种自动化、非侵入式的系统，用于室内环境中实时预测热舒适度。
• 评估低成本热成像摄像头（FLIR Lepton）在测量面部皮肤温度方面与可穿戴IButton传感器相比的可靠性。
• 探究在数据量充足的情况下，非辐射校准（伪彩色）热成像图像是否可用于预测个性化热舒适度。
• 利用机器学习融合热成像与可见光图像数据，以提升舒适度评估的准确性。

提出的方法

• 同时使用低成本FLIR Lepton摄像头和标准RGB摄像头捕获人员的热成像与可见光图像。
• 通过图像处理技术从热成像图像中提取面部多个区域的温度数据。
• 将热成像摄像头读数与佩戴在额头的IButton可穿戴传感器进行对比，以评估测量精度。
• 将热成像图像转换为伪彩色表示，以探索非辐射校准数据在舒适度预测中的实用性。
• 在融合的热成像与可见光图像数据上训练并比较机器学习模型——随机森林与K近邻模型，用于舒适度分类。
• 使用交叉验证和性能指标评估模型在多样化数据集上的准确性、精确度与泛化能力。

实验结果

研究问题

• RQ1 与可穿戴IButton传感器相比，低成本热成像摄像头（FLIR Lepton）能否可靠地测量面部皮肤温度？
• RQ2 在多大程度上，非辐射校准（伪彩色）热成像图像可以用于预测个性化热舒适度？
• RQ3 在从多模态图像数据预测热舒适度时，随机森林与K近邻模型中哪一个表现更优？
• RQ4 与单一模态方法相比，热成像与可见光图像数据的融合在多大程度上提升了热舒适度预测的准确性？

主要发现

• FLIR Lepton热成像摄像头与IButton可穿戴传感器表现出强相关性，证实其在非侵入式场景中测量面部温度的可靠性。
• 随机森林与K近邻模型在基于融合热成像与可见光图像数据预测个性化热舒适度方面均表现出高精度。
• 尽管缺乏辐射校准，但经过大规模数据集训练的非辐射校准热成像图像在热舒适度预测方面展现出潜力。
• 与单独使用任一模态相比，热成像与可见光图像数据的融合显著提升了预测性能。
• 本研究证实，机器学习模型能够有效从低成本、非侵入式成像系统中学习复杂的热舒适度模式。
• 研究结果支持在真实建筑环境中部署可扩展、自动化的热舒适度监测系统的可行性。