Grant: Discovery Grants Program - Individual
Carleton University
$58,000.00
Development of datasets, inverse models, and methods for adaptive fault detection and diagnostics in commercial buildings
FY 2022-23
Summary
30 to 50% of the energy use in commercial buildings is wasted due to poorly maintained, degraded, and improperly controlled equipment and components. Given that indoor climate control in commercial buildings accounts for 13% of the total energy use and 11% of the CO2 emissions in Canada, optimizing their operation represents great potential to reduce our environmental impact and to provide comfortable, healthy, and productive indoor environments. The overall objective of this research program is to optimize the energy use and occupant comfort in commercial buildings by using sensor, meter, and actuator data gathered in modern building automation and control systems. With this vision in mind, the research program will address four fundamental gaps in the literature: (1) create a dataset comprising common building faults, their sensory symptoms, occurrence frequencies, and impact on energy use and comfort; (2) develop inverse models that explain multiphysical processes and occupant behaviour in buildings from sensor, meter, and actuator data; (3) develop scalable methods to diagnose physical faults in building systems and components; and (4) develop scalable methods to diagnose and correct soft faults in controls programming. The research approaches entail field-scale data collection and analyses using existing controls and automation infrastructure of three office buildings in Carleton University, field trials, and building performance simulation. The proposed research program will make significant short-term and long-term intellectual, environmental, economic, and HQP contributions to Canada. New datasets, models, and methods will be created. These will help us understand how our buildings perform and are used today. Wider usage of fault detection and diagnostics methods developed in this research program will reduce the environmental and economic impact of buildings. Adoption of these methods by a Canadian building data analytics company will contribute to our knowledge-based economy. More importantly, two PhD, two MSc, and three undergraduate students will work on data from real buildings, learn their systems and components, and their shortcomings. In a team environment, they will conduct interdisciplinary research on building physics, indoor environmental quality, building performance simulation, and data-science. These skills are invaluable, as few engineering programs in Canada provide a comprehensive background in building engineering, despite buildings' major role in our economy and society.
Program
Discovery Grants Program - Individual
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
RGPIN-2017-06317
Name nom
Gunay, Burak
Department département
Civil and Environmental Engineering - Civil and Environmental Engineering
Organization id
23
Institution établissement
Carleton University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2017
Award amount
58000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1509
Committee name en
Civil, Industrial and Systems Engineering
Committee name fr
Génie civil, industriel et des systèmes
Area of application code
306
Area of application group en
Energy resources
Area of application group fr
Ressources d'énergie
Area of application en
Energy efficiency
Area of application fr
Efficacité énergétique
Research subject code
2100
Research subject group en
Mechanical engineering
Research subject group fr
Génie mécanique
Research subject en
Mechanical engineering
Research subject fr
Génie mécanique
Installment
inst:6;7
Partie
2
Nb partie
7
Application title
Development of datasets, inverse models, and methods for adaptive fault detection and diagnostics in commercial buildings
Application summary
30 to 50% of the energy use in commercial buildings is wasted due to poorly maintained, degraded, and improperly controlled equipment and components. Given that indoor climate control in commercial buildings accounts for 13% of the total energy use and 11% of the CO2 emissions in Canada, optimizing their operation represents great potential to reduce our environmental impact and to provide comfortable, healthy, and productive indoor environments. The overall objective of this research program is to optimize the energy use and occupant comfort in commercial buildings by using sensor, meter, and actuator data gathered in modern building automation and control systems. With this vision in mind, the research program will address four fundamental gaps in the literature: (1) create a dataset comprising common building faults, their sensory symptoms, occurrence frequencies, and impact on energy use and comfort; (2) develop inverse models that explain multiphysical processes and occupant behaviour in buildings from sensor, meter, and actuator data; (3) develop scalable methods to diagnose physical faults in building systems and components; and (4) develop scalable methods to diagnose and correct soft faults in controls programming. The research approaches entail field-scale data collection and analyses using existing controls and automation infrastructure of three office buildings in Carleton University, field trials, and building performance simulation. The proposed research program will make significant short-term and long-term intellectual, environmental, economic, and HQP contributions to Canada. New datasets, models, and methods will be created. These will help us understand how our buildings perform and are used today. Wider usage of fault detection and diagnostics methods developed in this research program will reduce the environmental and economic impact of buildings. Adoption of these methods by a Canadian building data analytics company will contribute to our knowledge-based economy. More importantly, two PhD, two MSc, and three undergraduate students will work on data from real buildings, learn their systems and components, and their shortcomings. In a team environment, they will conduct interdisciplinary research on building physics, indoor environmental quality, building performance simulation, and data-science. These skills are invaluable, as few engineering programs in Canada provide a comprehensive background in building engineering, despite buildings' major role in our economy and society.