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Machine learning for residential building HVAC analytics platform

Machine learning for residential building HVAC analytics platform
用于住宅建筑 HVAC 分析平台的机器学习
批准号:
508857-2017
负责人:
Sanner, Scott
金额:
$1.55万
依托单位:
依托单位国家:
加拿大
项目类别:
Collaborative Research and Development Grants
财政年份:
2017
资助国家:
加拿大
项目状态:
已结题
起止时间:
2017-01-01 至 2018-12-31

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中文摘要
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英文摘要
Residential building space conditions makes up 64% of the energy consumption of the average Canadian home. The optimization and improvement of these systems has progressed little in comparison to the buildings in commercial applications and the ever more connected home. As such there is a substantial opportunity to improve these systems in their operation and present homeowners opportunity to save both energy and money. The current project will use the relatively new, and untouched, data that has became more mainstream with advent of the connected thermostat. Now, for the first time really, long term operation, interaction and performance data can be collected at the individual home level. This data will allow for more advanced models, controls, monitoring and recommendations to be made for an individual home. To proceed with the proposed project, two leading researchers with complementary skills and experiences with collaborate with Canada-based ecobee inc. for a five-year term. The industry partner is a market leader in the residential connected thermostat space. They have the capacity and interest to learn from and potentially integrate the research results and develop them into marketable products and provide their customers increased value. Ecobee inc has committed to contributing significant resources to the project, including: technical advice/support, engineering services, hardware, computational assistance, and monetary funding. Data collected from operational units in the field across the ecobee population along with high quality modelling with provide the testbed for designed controls and machine learning products. The outcome of the research is systems able to learn from its environment (both system and occupants) and provide improved controls, recommendations on inefficiencies and diagnose anomalies in the performance. Each of these will increase the energy efficiency and potentially comfort of the occupants. Aside from helping Canada achieve its objectives in greenhouse gas emissions and reducing the need for additional energy infrastructure, the developed building controls will help the industry partner remain competitive within Canada and abroad.
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