Reference number : GC-2017-Q2-00443, amendment number : , agreement type : G, recipient legal name : Atia, Mohamed Maher Mohamed (Carleton University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Per Statistics Canada, households continued to be the largest users of energy accounting for 24.0% of national_x000D_
energy use. Heating, ventilation, and air-conditioning (HVAC) has been singled out as the most important_x000D_
contributor to gas emissions. This project aims at enhancing residential HVAC systems to optimize energy_x000D_
consumption based on intelligent occupancy detection/identification. While several technologies/sensors have_x000D_
been proposed to address this challenge, the cost, accuracy, and the seamless integration with existing building_x000D_
automation infrastructures remain major challenges especially for homes where cost and complexity are key_x000D_
factors. Recently, there is a noticeable increase in number and types of affordable sensors in modern homes._x000D_
For example, solar radiation sensors are used to detect room lightening condition. Infrared and ultrasound_x000D_
sensors are used as proximity and motion sensors. Weight sensors are becoming common in several houses._x000D_
Some advanced HVAC systems provides level of carbon dioxide. The availability of these sensors creates an_x000D_
opportunity to develop cost-effective advanced occupancy detection and identification technology to enable_x000D_
optimized adaptive residential HVAC systems. The proposed project will adopt a sensor fusion approach to_x000D_
tackle the problem. In one possible scenario, radar and silicon MEMS-based microphone sensors may be used_x000D_
to develop occupancy maps. Radar and silicon MEMS-based microphone sensors provide far field sound_x000D_
waves capture by audio beam-forming combined with radar target presence detection. With the increasing_x000D_
number of other available sensors, artificial intelligence and machine learning can be used to apply sensor_x000D_
fusion. The proposed system will have two phases; training phase and identification phase. In training phase,_x000D_
occupancy and identity data will be collected along with available sensor measurements. Data will be modeled_x000D_
using a Hidden Markov Model. In identification phase, first, the available sensor measurements will be used to_x000D_
detect occupancy. Then, the radar/MEMS-based microphone can be will be used to identify the occupants_x000D_
using voice recognition., program purpose french : Per Statistics Canada, households continued to be the largest users of energy accounting for 24.0% of national_x000D_
energy use. Heating, ventilation, and air-conditioning (HVAC) has been singled out as the most important_x000D_
contributor to gas emissions. This project aims at enhancing residential HVAC systems to optimize energy_x000D_
consumption based on intelligent occupancy detection/identification. While several technologies/sensors have_x000D_
been proposed to address this challenge, the cost, accuracy, and the seamless integration with existing building_x000D_
automation infrastructures remain major challenges especially for homes where cost and complexity are key_x000D_
factors. Recently, there is a noticeable increase in number and types of affordable sensors in modern homes._x000D_
For example, solar radiation sensors are used to detect room lightening condition. Infrared and ultrasound_x000D_
sensors are used as proximity and motion sensors. Weight sensors are becoming common in several houses._x000D_
Some advanced HVAC systems provides level of carbon dioxide. The availability of these sensors creates an_x000D_
opportunity to develop cost-effective advanced occupancy detection and identification technology to enable_x000D_
optimized adaptive residential HVAC systems. The proposed project will adopt a sensor fusion approach to_x000D_
tackle the problem. In one possible scenario, radar and silicon MEMS-based microphone sensors may be used_x000D_
to develop occupancy maps. Radar and silicon MEMS-based microphone sensors provide far field sound_x000D_
waves capture by audio beam-forming combined with radar target presence detection. With the increasing_x000D_
number of other available sensors, artificial intelligence and machine learning can be used to apply sensor_x000D_
fusion. The proposed system will have two phases; training phase and identification phase. In training phase,_x000D_
occupancy and identity data will be collected along with available sensor measurements. Data will be modeled_x000D_
using a Hidden Markov Model. In identification phase, first, the available sensor measurements will be used to_x000D_
detect occupancy. Then, the radar/MEMS-based microphone can be will be used to identify the occupants_x000D_
using voice recognition., agreement title english : Occupancy-based Adaptive HVAC Control for Energy Efficient Residential Buildings, agreement title french : Occupancy-based Adaptive HVAC Control for Energy Efficient Residential Buildings, agreement number : EGP, agreement value : 25000.0, 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