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Machine learning method for real-time non-invasive prediction of individual thermal preference in transient conditions

机译：机器学习方法，用于在瞬态条件下实时无创地预测个体的热偏好

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摘要

This work introduces a new technique that provides real-time feedback to a Heating, Ventilation, and Air Conditioning (HVAC) system controller with respect to the occupants' thermal preferences to avoid space overheating. We propose a non-invasive approach for automatic prediction of personal thermal comfort and mean time to warm discomfort using machine learning. The prediction framework described uses temperature information extracted from multiple local body parts to model an individual's thermal preference, with sensing measurements that capture local body part variance as well as differences between body parts. We compared the efficacy of using machine learning with classical measurements such as skin temperature along with our approach of using multi-part measurements and derived data. An analysis of the performance of machine learning shows that our method improved the accuracy of personal thermal comfort prediction by an average of 60%, and the accuracy of mean time to warm discomfort prediction by an average of 40%.The proposed thermal models were tested on subjects' data extracted from an office setup with room temperature varying from low (21.11 degrees C) to high (27.78 degrees C). When all proposed features were used, personal thermal comfort was predicted with an accuracy higher than 80% and mean time to warm discomfort with more than 85% accuracy. Further analysis of the machine learning efficacy showed that temperature differences had the highest impact on performance of individual thermal preference prediction, while the proposed approach was found not sensitive to the actual machine learning algorithm.
机译：这项工作引入了一项新技术，该技术可根据乘员的喜好向加热，通风和空调（HVAC）系统控制器提供实时反馈，以避免空间过热。我们提出了一种非侵入性方法，用于通过机器学习自动预测个人的热舒适度和平均时间来温暖不适。所描述的预测框架使用从多个局部身体部位提取的温度信息来建模个人的热偏好，并通过感测测量来捕捉局部身体部位的变异以及身体部位之间的差异。我们比较了使用机器学习和经典测量（例如皮肤温度）的功效，以及使用多部分测量和导出数据的方法。对机器学习性能的分析表明，我们的方法将个人热舒适性预测的准确性平均提高了60％，将平均温暖不适时间的平均准确性提高了40％。从办公室设置中提取的受试者数据，室温从低（21.11摄氏度）到高（27.78摄氏度）不等。当使用所有建议的功能时，可预测个人热舒适度的准确性高于80％，平均温暖不适时间的准确性高于85％。机器学习功效的进一步分析表明，温度差异对单个热偏好预测的性能影响最大，而发现该方法对实际的机器学习算法不敏感。

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