School of Emergency Management & Safety Engineering, China University of Mining and Technology, Beijing, 100083, China.
School of Emergency Management & Safety Engineering, China University of Mining and Technology, Beijing, 100083, China.
J Therm Biol. 2023 Feb;112:103484. doi: 10.1016/j.jtherbio.2023.103484. Epub 2023 Jan 25.
Human thermal comfort is relevant to human life comfort and plays a pivotal role in occupational health and thermal safety. To ensure that intelligent temperature-controlled equipment can deliver a sense of cosiness to people while improving its energy efficiency, we designed a smart decision-making system that sets the thermal comfort adjustment preference as a label, reflecting both the human body's thermal feeling and its acceptance of the thermal environment. By training a series of supervised learning models underpinned by environmental and human features, the most appropriate adjustment mode in the current environment was predicted. To bring this design into reality, we tried six supervised learning models, and then, by comparison and evaluation, we identified that the Deep Forest's performance was the best. The model takes into account objective environmental factors and human body parameters. In this way, it can achieve high accuracy in application and good simulation and prediction results. The results can provide feasible references for feature selection and model selection in further research with the aim of testing thermal comfort adjustment preference. The model can provide recommendations for the thermal comfort preference in a specific place at a particular time, as well as guidance on human thermal comfort preference and thermal safety precautions in specific occupational groups.
人类热舒适与人类生活舒适度息息相关，在职业健康和热安全方面起着关键作用。为了确保智能温控设备在提高能源效率的同时，能为人们带来舒适感，我们设计了一个智能决策系统，将热舒适调整偏好设定为标签，反映人体的热感觉和对热环境的接受程度。通过训练一系列基于环境和人体特征的监督学习模型，预测当前环境下最合适的调整模式。为了将这一设计变为现实，我们尝试了六种监督学习模型，然后通过比较和评估，我们发现 Deep Forest 的性能最佳。该模型考虑了客观环境因素和人体参数。这样，它可以在应用中实现高精度，并且具有良好的模拟和预测结果。研究结果可为进一步研究中的特征选择和模型选择提供可行的参考，旨在测试热舒适调整偏好。该模型可以为特定地点和特定时间的热舒适偏好提供建议，以及为特定职业群体的人类热舒适偏好和热安全预防措施提供指导。
