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

虽然混合模式建筑结合了可操作建筑围护结构和机械系统的自然通风在加拿大很常见，但从操作顺序的设计中忽略了居住者的窗户和恒温器的使用，导致自然通风潜力的不适当利用和慢性能源浪费。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占用率和乘员舒适度数据，代表了提高能源效率的未开发机会。然而，在寒冷气候条件下，混合模式建筑的OCC算法尚未得到发展。为此，本项目将与新加坡国立大学的混合模式建筑运营和设计专家Adrian Chong博士合作，为混合模式建筑开发和展示新颖的行为推动控制算法。这些算法将通过用户的恒温器和窗户使用模式来学习用户的温度偏好，并动态地诱导对室内温度设定值的个性化调整，如果这样做可以减少空间冷却需求，则提示居住者打开窗户，如果这样做可以减少空间加热或冷却需求，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，包括34个周边办公空间，这些办公空间带有可操作的窗户，并配有建筑自动化系统集成的接触传感器。该项目的主要成果是一套控制算法，最大限度地提高加拿大混合模式建筑的自然通风潜力。算法的开发和演示将通过期刊、会议和杂志论文进行传播。一名博士生和一名硕士学生将接受建筑控制和自动化、热舒适、数据分析和逆建模方面的高级技能培训。该算法有望将寒冷气候下商业建筑的供暖和制冷能源消耗减少20%。该项目还将与钟博士在更广泛的OCC技术领域建立长期的研究伙伴关系，以提高建筑物的可持续性和健康。

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.