Occupant-centric control algorithms for mixed-mode buildings in cold climates

虽然混合模式建筑将可操作建筑围护结构和机械系统的自然通风结合在一起在加拿大很常见，但在操作顺序的设计中忽视居住者的窗户和恒温器使用会导致对自然通风潜力的不适当利用和长期的能源浪费。以乘员为中心的控制(OCC)是一种室内气候控制方法，通过在操作序列中使用乘员和乘员舒适度数据，代表着一个尚未开发的提高能源效率的机会。然而，针对寒冷气候下混合模式建筑的OCC算法还没有开发出来。为此，该项目将与新加坡国立大学的禤浩焯博士合作，开发和演示适用于混合模式建筑的新颖的行为轻推控制算法。这些算法将通过用户的恒温器和窗户使用模式来了解用户的温度偏好，并动态诱导对室内温度设置点的个性化调整，如果这样做减少了空间制冷需求，则提示居住者打开窗户，如果这样做减少了空间供暖或制冷需求，则关闭窗户。这些算法将在一个生活实验室设施中进行现场测试，其中包括34个周边办公空间，这些空间带有可操作的窗户，配备了集成了建筑自动化系统的接触传感器。该项目的主要成果是一套控制算法，使加拿大混合模式建筑的自然通风潜力最大化。算法的开发和演示将通过期刊、会议和杂志论文进行传播。博士生和理科硕士。学生将接受建筑控制和自动化、热舒适性、数据分析和逆向建模方面的高级技能培训。这些算法预计将使寒冷气候下商业建筑的供暖和制冷能耗减少20%。该项目还将导致与庄博士在更广泛的OCC技术领域建立长期研究伙伴关系，以改善建筑的可持续性和健康。

While mixed-mode buildings that combine natural ventilation from operable building envelopes and mechanical systems are common in Canada, disregard of occupants' window and thermostat use from the design of sequences of operation leads to inappropriate utilization of natural ventilation potential and chronic energy waste. Occupant-centric control (OCC), an indoor climate control approach whereby occupancy and occupant comfort data are used in the sequences of operation, represents an untapped opportunity to improve energy efficiency. However, OCC algorithms for mixed-mode buildings in cold climates have not been developed. To this end, in collaboration with Dr. Adrian Chong from the National University of Singapore, an expert in the operation and design of mixed-mode buildings, this project will develop and demonstrate novel behaviour nudging control algorithms for mixed-mode buildings. These algorithms will learn users' temperature preferences through their thermostat and window use patterns and dynamically induce personalized adjustments to the indoor temperature setpoints, prompting occupants to open their windows if doing so reduces the space cooling needs and to close their windows when doing so reduces the space heating or cooling needs. The algorithms will be field-tested in a living-lab facility, including 34 perimeter office spaces with operable windows instrumented with building automation system-integrated contact sensors. The primary outcome of this project is a suite of control algorithms maximizing the natural ventilation potential in mixed-mode buildings in Canada. Development and demonstration of the algorithms will be disseminated via journal, conference, and magazine papers. A Ph.D. student and an M.Sc. student will be trained with advanced skills in building controls and automation, thermal comfort, data analytics, and inverse modelling. The algorithms are expected to reduce heating and cooling energy use in commercial buildings in cold climates by 20%. The project will also lead to a long-term research partnership with Dr. Chong in the broader area of OCC technologies improving sustainability and wellness in buildings.