Reference number : GC-2017-Q2-00455, amendment number : , agreement type : G, recipient legal name : Sanner, Scott (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Collaborative Research and Development Grants - Project, program name french : Subventions de recherche et développement coopérative - projet, program purpose english : 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., program purpose french : 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., agreement title english : Machine learning for residential building HVAC analytics platform, agreement title french : Machine learning for residential building HVAC analytics platform, agreement number : CRDPJ, agreement value : 106670.0, agreement start date : 2017-08-23, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2022-2023)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2022-2023)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada