Reference number : GC-2017-Q1-03285, amendment number : , agreement type : G, recipient legal name : Gagnon, Ghyslain (École de technologie supérieure), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Energy disaggregation (also referred as nonintrusive load monitoring) is a combination of signal processing and pattern recognition techniques to estimate the energy consumption of individual appliances from the total energy consumption signal. It is achieved by identifying discriminating features in the aggregated signal and decomposing it into its constituent parts. Disaggregation of the energy consumption data down to the level of appliances has been largely identified as an opportunity for enhanced energy efficiency through citizen awareness, energy demand prediction tools for utilities and smart automatic control of appliances, just to name a few.
 
 
This research program seeks to advance the field of energy disaggregation to efficiently determine the power consumption of individual electrical loads in real-life scenarios. This will be achieved by building on the applicant&rsquo;s team latest developments in Hall-effect current sensor devices and weakly-supervised machine learning algorithms. Specifically, we will 1) design new energy disaggregation algorithms for residential applications using weakly labeled data from low-precision current sensors, 2) analyze the sensitivity of the disaggregation algorithms to the accuracy and quantity of sensor information, 3) design the next generation of ultra-low-power Hall-effect current sensors and 4) design new energy disaggregation algorithms for commercial and industrial applications with an optimal number of low-precision sensors. These advances will increase the accuracy, the scalability and the adaptability of existing techniques.
 
 
This research program will train four HQP in domains which are in high demand in industry and academia, gaining important skills in signal processing, machine learning and microelectronics. The research results could also be the starting point of new collaborations, as energy disaggregation is gaining interest from industry and utilities, as confirmed by recent important investments in this field.
 
 
Natural Resources Canada established that "the Canadian buildings sector has a duty to use our energy resources responsibly and take up the call to action as a mechanism that will strengthen and enrich our economy for future generations." This research program is an important step in that direction, through the development of novel technologies to monitor energy consumption more efficiently, and eventually, enabling leading-edge energy management technologies for smart buildings., program purpose french : Energy disaggregation (also referred as nonintrusive load monitoring) is a combination of signal processing and pattern recognition techniques to estimate the energy consumption of individual appliances from the total energy consumption signal. It is achieved by identifying discriminating features in the aggregated signal and decomposing it into its constituent parts. Disaggregation of the energy consumption data down to the level of appliances has been largely identified as an opportunity for enhanced energy efficiency through citizen awareness, energy demand prediction tools for utilities and smart automatic control of appliances, just to name a few.
 
 
This research program seeks to advance the field of energy disaggregation to efficiently determine the power consumption of individual electrical loads in real-life scenarios. This will be achieved by building on the applicant&rsquo;s team latest developments in Hall-effect current sensor devices and weakly-supervised machine learning algorithms. Specifically, we will 1) design new energy disaggregation algorithms for residential applications using weakly labeled data from low-precision current sensors, 2) analyze the sensitivity of the disaggregation algorithms to the accuracy and quantity of sensor information, 3) design the next generation of ultra-low-power Hall-effect current sensors and 4) design new energy disaggregation algorithms for commercial and industrial applications with an optimal number of low-precision sensors. These advances will increase the accuracy, the scalability and the adaptability of existing techniques.
 
 
This research program will train four HQP in domains which are in high demand in industry and academia, gaining important skills in signal processing, machine learning and microelectronics. The research results could also be the starting point of new collaborations, as energy disaggregation is gaining interest from industry and utilities, as confirmed by recent important investments in this field.
 
 
Natural Resources Canada established that "the Canadian buildings sector has a duty to use our energy resources responsibly and take up the call to action as a mechanism that will strengthen and enrich our economy for future generations." This research program is an important step in that direction, through the development of novel technologies to monitor energy consumption more efficiently, and eventually, enabling leading-edge energy management technologies for smart buildings., agreement title english : Advanced devices and algorithms for energy disaggregation in buildings, agreement title french : Advanced devices and algorithms for energy disaggregation in buildings, agreement number : RGPIN, agreement value : 120000.0, agreement start date : 2017-05-10, 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