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Non-Intrusive Interpretation and Improvement of Multi-Occupancy Human Thermal Comfort through Analysis of Facial Infrared Thermography

通过面部红外热成像分析非侵入式解释和改善多人人体热舒适度

基本信息

批准号：
1804321
负责人：
Carol Menassa
金额：
$ 36.5万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1804321&HistoricalAwards=false
关键词：
Non Intrusive Interpretation Improvement Multi

项目摘要

In the U.S. and worldwide, HVAC systems represent one of the largest energy end uses, accounting for approximately 50 percent of the total energy required to operate residential and commercial buildings. Despite the significant energy footprint of HVAC systems, occupants in the built environment are often dissatisfied with their thermal comfort. Current "human-in-the-loop" approaches provide opportunities for occupants to vote on thermal comfort and preferences (for example, by adjusting the thermostat), thus allowing for HVAC system adjustment based on human feedback. However, relying on intermittent human feedback prevents robust evaluation of the comfort level and determination of a comfortable setpoint. This project will explore the feasibility of using infrared thermography as a non-intrusive method for predicting human thermal comfort preferences in single and multi-occupancy building spaces. It will also design and validate a robust HVAC control framework for buildings that will synchronously use the analyzed thermography data to adjust its setpoint to improve thermal comfort in indoor spaces and reduce overall dissatisfaction among occupants in multi-occupancy spaces.The project aims to explore the premise that thermal comfort can be measured non-intrusively and reliably in real, operational built environments. The resulting new knowledge has the potential to transition building HVAC control from a passive and user-empirical process to an automated, user-centric and data-driven mechanism that can simultaneously improve occupant satisfaction in indoor environments while reducing energy consumption. The research extends theory from human thermal comfort evaluation, computer vision and optimization under uncertainties with the objective of creating a robust and scalable non-intrusive HVAC control framework for thermal comfort optimization in various indoor contexts. Although the focus is on thermal comfort in this project, the developed framework and methodology can be extended to evaluate other indoor environmental quality factors such as lighting and airflow. This project will also build a publicly available thermal comfort dataset consisting of facial thermal images, subjective thermal sensations and preferences of occupants, and ambient room conditions, that will enable consistent evaluation and benchmarking of new methods in the future.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在美国和全球范围内，HVAC系统是最大的能源终端用途之一，约占住宅和商业建筑运营所需总能源的50%。尽管HVAC系统的能源足迹显著，但建筑环境中的居住者往往对其热舒适性不满意。当前的“人在回路”方法为居住者提供了对热舒适性和偏好进行投票的机会（例如，通过调节恒温器），从而允许基于人类反馈来调节HVAC系统。然而，依赖于间歇性的人类反馈阻止了对舒适度水平的鲁棒评估和对舒适设定点的确定。本项目将探索使用红外热成像作为一种非侵入性方法来预测单人和多人居住建筑空间中人体热舒适偏好的可行性。该项目还将为建筑设计和验证一个强大的HVAC控制框架，该框架将同步使用分析的热成像数据来调整其设定值，以改善室内空间的热舒适度，并减少多人居住空间中居住者的总体不满。该项目旨在探索在真实的可操作建筑环境中可以非侵入性和可靠地测量热舒适度的前提。由此产生的新知识有可能将建筑HVAC控制从被动和用户经验过程转变为自动化，以用户为中心和数据驱动的机制，可以同时提高室内环境中的居住者满意度，同时降低能耗。该研究扩展了人体热舒适评估，计算机视觉和不确定性下的优化理论，旨在创建一个强大的，可扩展的非侵入式HVAC控制框架，用于各种室内环境中的热舒适优化。虽然重点是在这个项目中的热舒适性，开发的框架和方法可以扩展到评估其他室内环境质量因素，如照明和气流。该项目还将建立一个公开可用的热舒适数据集，包括面部热图像、主观热感觉和居住者的偏好，以及周围的房间条件，这将有助于在未来对新方法进行一致的评估和基准测试。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。