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

Occupant-centric control algorithms for mixed-mode buildings in cold climates
寒冷气候下混合模式建筑的以居住者为中心的控制算法
批准号:
578499-2022
负责人:
Gunay, BurakHB
金额:
$1.82万
依托单位:
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31

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中文摘要
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英文摘要
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.
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