Developing occupant-centric control sequences for thermostat control in buildings
Developing occupant-centric control sequences for thermostat control in buildings
批准号:
552694-2020
负责人:
Ouf, Mohamed
金额:
$1.46万
依托单位:
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2020
资助国家:
加拿大
项目状态:
已结题
起止时间:
2020-01-01 至 2021-12-31
中文摘要
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英文摘要
The proposed partnership between researchers at Concordia University and BrainBox AI, a Montreal-based leader in building automation, aims to develop novel algorithms for smart building controls. The goal of these new "occupant-centric control" algorithms is to balance between energy efficiency and occupant comfort by using machine learning techniques to learn and predict occupancy patterns and occupant preferences. They will provide significant tangible reductions in buildings' energy use and CO2 emissions, and complement the partner organization's existing libraries of solutions and algorithms for smart building controls. With access to more than 18 million building square feet of buildings across different sectors in Canada, the partner organization will leverage the outcomes of this research to gain insight and methodologies on approaches for learning and predicting occupancy and occupant preferences. This can position them as a global leader in developing artificial intelligence (AI) technology designed specifically for building systems. Research outcomes can ultimately be easily deployed at minimal capital cost in many of Canada's over half a million commercial buildings, which collectively consume approximately 40% of energy use in Canada. In light of the current COVID-19 pandemic, the proposed research would also allow buildings to quickly self-adapt to reduced occupancy levels in commercial buildings and reduce their energy consumption accordingly, especially as plans for re-opening the economy will likely limit occupancy in work environments. Furthermore, this partnership will provide Highly Qualified Personnel (HQP) with a unique opportunity to gain relevant industry experience that will allow them to fully understand building systems and controls technologies related to their research. They will be able to work with data from real buildings and learn about their systems and components, while gaining interdisciplinary research skills combining building engineering with building sciences; both of which are strongly and urgently needed in the Canadian building industry.
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会议论文
Optimizing urban-scale energy use with uncertainty from occupant behaviour
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批准号:RGPIN-2020-06804
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项目类别:Discovery Grants Program - Individual
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资助金额:$2.26万
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财政年份:2022
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负责人:Ouf, Mohamed
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依托单位:
Optimizing urban-scale energy use with uncertainty from occupant behaviour
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批准号:RGPIN-2020-06804
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项目类别:Discovery Grants Program - Individual
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资助金额:$2.26万
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财政年份:2021
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负责人:Ouf, Mohamed
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依托单位:
Implementation of novel occupant-centric control strategies in commercial buildings
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批准号:568511-2021
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项目类别:Alliance Grants
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资助金额:$1.46万
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财政年份:2021
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负责人:Ouf, Mohamed
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依托单位:
Optimizing urban-scale energy use with uncertainty from occupant behaviour
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批准号:DGECR-2020-00415
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项目类别:Discovery Launch Supplement
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资助金额:$0.91万
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财政年份:2020
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负责人:Ouf, Mohamed
-
依托单位:
Optimizing urban-scale energy use with uncertainty from occupant behaviour
-
批准号:RGPIN-2020-06804
-
项目类别:Discovery Grants Program - Individual
-
资助金额:$2.26万
-
财政年份:2020
-
负责人:Ouf, Mohamed
-
依托单位:
海外基金