Optimizing Indoor Environment Quality through Quantifying Human Experience using Ubiquitous Sensing

目前，建筑部门（即商业和住宅）占加拿大总能源使用量的25%以上。这种高能耗与气候变化的快速发展相结合，推动了全球对高性能建筑的推动，通过调试高效和高度感知的建筑控制系统，如采暖、通风和空调（HVAC），来减少能源消耗。然而，这些日益密闭性和节能的建筑给室内环境质量（IEQ）条件带来了巨大的变化，即室内环境质量因素的配置，包括热条件、空气质量、声学和视觉舒适[2]。这些变化对人类在建筑中的体验提出了挑战（即，我们的生理、情感和认知状态所反映的精神状态）。由于人们超过85%的时间是在室内度过的，因此环境质量因素对建筑物居住者的健康和福祉起着至关重要的作用。尽管专业协会和标准化组织努力制定改善建筑设计和运营的指导方针（例如，LEED），但衡量人类经验的普遍方法（例如，入住后调查）仍然是主观的和事后的。本研究项目旨在通过实验方法测量人类受试者在各种IEQ条件下的生理反应，加深我们对室内人类体验的理解，从而改变现状。我打算通过三个短期目标来实现这一总体目标：(1)建立一个能够提供各种IEQ因素配置的实验室系统，并测量受试者的环境状态和生理反应；(2)进行人体实验，收集环境和生理数据，量化不同的IEQ因素对人类体验的影响；(3)利用收集到的数据，建立基于机器学习的人类体验主观评分预测模型，并开发基于强化学习的控制算法，该算法可以操纵IEQ因素，以最大限度地减少能源消耗，同时改善室内人类体验。这个项目的成果可以被加拿大各地的建筑师、工程师和设施管理人员在资本项目的各个阶段利用，以改善建筑设计和运营，目标是提高可持续性和建筑居住者的健康和福祉。参与本研究计划的高素质人才（HQP）将接受实验设计、数据收集和管理、高级统计和数据分析的机器学习技术等市场技能的培训，这些都是土木和机械工程领域非常需要的。

Currently, the building sector (i.e., commercial and residential) accounts for more than 25% of the total energy use in Canada [1]. This high energy use combined with the rapid progression of climate change has contributed to a global push for high-performance buildings to curtail energy consumption, by commissioning high-efficiency and highly sensed building control systems such as Heating, Ventilation, and Air Conditioning (HVAC). However, these increasingly air-tight, energy conserving buildings bring dramatic changes to Indoor Environment Quality (IEQ) conditions, which are configurations of IEQ factors including thermal conditions, air quality, acoustic, and visual comfort [2]. These changes pose challenges to human experiences in buildings (i.e., the state of mind that is reflected by our physiological, emotional, and cognitive statuses). Because people spend more than 85% of their time indoors [3], IEQ factors play an essential role in the health and well-being of building occupants. Despite efforts from professional societies and standardization organizations to develop guidelines for improving building design and operation (e.g., LEED), the prevalent method of gauging human experience (e.g., post occupancy surveys) remains subjective and after-the-fact. This research program aims to shift the status quo by deepening our understanding of human experience indoors, through an experimental approach that measures human subjects' physiological responses under various IEQ conditions. I intend to achieve this overarching goal through three short-term objectives: (1) build a laboratory system capable of delivering various configurations of IEQ factors, as well as measuring the environmental states and physiological responses of subjects; (2) conduct human subject experiments and collect environmental and physiological data to quantify the impact of different IEQ factors on human experience; and (3) build machine leaning-based prediction models for subjective ratings of human experience, using collected data, and develop an reinforcement learning-based control algorithm that manipulates IEQ factors to minimize energy consumption while improving human experience indoors. Outcomes from this program can be leveraged by architects, engineers, and facility managers across Canada at various stages of a capital project to improve building design and operation, with the goal of improving sustainability and the health and well-being of building occupants. Highly Qualified Personnel (HQP) involved in this research program will be trained for the marketable skills of experiment design, data collection and management, advanced statistics, and machine learning techniques for data analysis, all highly desirable in the field of civil and mechanical engineering.