Abstract The automatic control of the existing indoor thermal environment is usually based on the user's set points without considering their real-time thermal feeling, causing an uncomfortable thermal environment and high energy consumption. In this study, the wristband device's physiological data is utilized to propose a thermal sensation prediction-based fuzzy control method of indoor temperature to improve the indoor environment's thermal comfort and achieve an energy saving of air-conditioning system. The linear regression model-based updating method is utilized to update the thermal sensation prediction model in real-time. Moreover, Mamdani fuzzy model and FFSI method are adopted to develop the fuzzy control algorithm for indoor temperature set point optimization. Several comparative experiments are performed to compare control performance, thermal comfort, and energy consumption of thermal sensation prediction-based control with thermal sensation feedback-based control and temperature set point-based control. The results show that thermal sensation prediction-based control can adjust the temperature set point by monitoring physiological data from the subjects without interfering with their regular works. It is also revealed that the thermal comfort performance of thermal sensation prediction-based control is superior to the temperature set point-based control, while it is similar to the thermal sensation feedback-based control. Furthermore, the proposed control approach provides 20.07% and 10.73% savings in daily energy consumption compared with the temperature set point-based control and thermal sensation feedback-based control, respectively.

中文翻译：

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一种基于腕带装置的室内温度模糊控制热舒适节能方法的实验研究

摘要 现有室内热环境的自动控制通常基于用户的设定值，而没有考虑用户的实时热感，造成热环境不舒适，能耗高。本研究利用腕带设备的生理数据，提出了一种基于热感觉预测的室内温度模糊控制方法，以提高室内环境的热舒适度，实现空调系统的节能。利用基于线性回归模型的更新方法实时更新热感觉预测模型。此外，采用Mamdani模糊模型和FFSI方法来开发室内温度设定点优化的模糊控制算法。进行了几个对比实验，以比较基于热感觉预测的控制与基于热感觉反馈的控制和基于温度设定点的控制的控制性能、热舒适性和能耗。结果表明，基于热感觉预测的控制可以通过监测受试者的生理数据来调整温度设定点，而不会干扰他们的正常工作。还揭示了基于热感觉预测的控制的热舒适性能优于基于温度设定点的控制，而它与基于热感觉反馈的控制相似。此外，建议的控制方法提供 20.07% 和 10。