Reference number : GC-2017-Q2-00511, amendment number : , agreement type : G, recipient legal name : Evins, Ralph (University of Victoria), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : The SmartEMS project will develop data analysis and machine learning approaches suitable for incorporation_x000D_
in non-residential building energy management systems, and test them in real buildings. Dramatic recent_x000D_
improvements in the power of machine learning have made it possible to train and deploy such algorithms to_x000D_
solve practical challenges. Complex energy systems in buildings present many such challenges, from set-point_x000D_
optimization to predictive control. SES Inc. has the capabilities and client-base to take advantage of this._x000D_
There are two core approaches: offline learning from data, and real-time predictive control. The former will_x000D_
identify underlying trends and areas of concern by analysis of operational data; the latter will develop and train_x000D_
machine learning controllers that will improve operation based on weather predictions. The two together can_x000D_
deliver a highly flexible, robust solution._x000D_
SmartEMS will use open-source systems and protocols (VOLTTRON, BACnet) that have been successfully_x000D_
used by SES Inc. on previous projects. Open-source state of the art machine learning libraries based in Python_x000D_
(scikit-learn, TensorFlow) will be used, along with other Python-based data analysis and visualisation libraries._x000D_
Remotely accessible interface hardware (bare-bones PCs; Raspberry Pi) will be deployed in 3 test buildings_x000D_
(one university campus and two clients of SES Inc.). Cloud computing from CANARIE will be used for_x000D_
computationally intensive parts of the process._x000D_
The output will be a commercially deployable solution based on the latest academic research; parts of this will_x000D_
also be released as open-source. This will form the basis for an ongoing collaboration. The concept has_x000D_
significant potential to improve energy use, emissions and comfort in commercial buildings., program purpose french : The SmartEMS project will develop data analysis and machine learning approaches suitable for incorporation_x000D_
in non-residential building energy management systems, and test them in real buildings. Dramatic recent_x000D_
improvements in the power of machine learning have made it possible to train and deploy such algorithms to_x000D_
solve practical challenges. Complex energy systems in buildings present many such challenges, from set-point_x000D_
optimization to predictive control. SES Inc. has the capabilities and client-base to take advantage of this._x000D_
There are two core approaches: offline learning from data, and real-time predictive control. The former will_x000D_
identify underlying trends and areas of concern by analysis of operational data; the latter will develop and train_x000D_
machine learning controllers that will improve operation based on weather predictions. The two together can_x000D_
deliver a highly flexible, robust solution._x000D_
SmartEMS will use open-source systems and protocols (VOLTTRON, BACnet) that have been successfully_x000D_
used by SES Inc. on previous projects. Open-source state of the art machine learning libraries based in Python_x000D_
(scikit-learn, TensorFlow) will be used, along with other Python-based data analysis and visualisation libraries._x000D_
Remotely accessible interface hardware (bare-bones PCs; Raspberry Pi) will be deployed in 3 test buildings_x000D_
(one university campus and two clients of SES Inc.). Cloud computing from CANARIE will be used for_x000D_
computationally intensive parts of the process._x000D_
The output will be a commercially deployable solution based on the latest academic research; parts of this will_x000D_
also be released as open-source. This will form the basis for an ongoing collaboration. The concept has_x000D_
significant potential to improve energy use, emissions and comfort in commercial buildings., agreement title english : SmartEMS: Applying machine learning in building energy management systems, agreement title french : SmartEMS: Applying machine learning in building energy management systems, agreement number : EGP, agreement value : 24973.0, 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 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., 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