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Grant: Collaborative Research and Development Grants

University of Ontario Institute of Technology

$74,000.00

Intelligent single-point energy consumption monitoring for intensive energy sectors applications

EF 2012-13

Résumé
Smart meters are considered key component in the deployment of automatic metering infrastructure (AMI) in the smart grid evolution. Despite such meters' capabilities in reporting the total energy consumption along with time of use (TOU) to electric utilities and consumers, there is still lack of effective processes and methodologies to minimize the energy waste in intensive energy sectors such as industrial facilities, commercial buildings, university campuses, etc. Such large sectors are characterized by stranded equipment, which makes monitoring the electric energy consumption at individual equipment level from economic and practical perspectives infeasible. Given this fact, the process of identifying the energy waste in specific equipment, selecting candidate loads for demand side provision, equipment troubleshooting, and finding optimal operating strategies for this equipment considering TOU while ensuring not to compromise the whole sector goals/objectives become challenging to realize. Substantial research effort is needed to achieve a non-intrusive and cost-effective energy consumption monitoring approach that paves the road toward realizing smarter, efficient, and accurate metering infrastructure. The proposed research work aims to bridge the gap between existing energy monitoring system and smart energy consumption through the development of intelligent algorithms for AMI based on single-point sensing and innovative un-supervised learning to breakdown the aggregated energy consumption in the targeted sectors hence better quantify the electricity usage and identify the energy waste. With close co-operation with the industrial partners of this project, the proposed research work will supply Hydro One with the necessary information that will help reducing the impact on their transmission/distribution systems, increase the lifetime/performance of their assets, expand the capabilities of the energy monitoring products of Siemens Canada while ensuring low product cost and more energy consumption information accessibility. Moreover, the proposed research work will also help Ontario Power Generation to reduce their generation production cost and reduce their carbon foot print.
Programme
Collaborative Research and Development Grants
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
437388-2012
Name nom
Ibrahim, WalidMorsi
Department département
Engineering and Applied Science, Faculty of
Organization id
19222
Institution établissement
University of Ontario Institute of Technology
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2012
Competition year année de concours
2012
Award amount
74000
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
304
Area of application group en
Energy resources
Area of application group fr
Ressources d'énergie
Area of application en
Electrical energy
Area of application fr
Énergie électrique
Research subject code
2501
Research subject group en
Electrical and electronic engineering
Research subject group fr
Génie électrique et électronique
Research subject en
Power systems
Research subject fr
Réseaux électriques
Partie
1
Nb partie
3
Application title
Intelligent single-point energy consumption monitoring for intensive energy sectors applications
Keyword
NSERC
Application summary
Smart meters are considered key component in the deployment of automatic metering infrastructure (AMI) in the smart grid evolution. Despite such meters' capabilities in reporting the total energy consumption along with time of use (TOU) to electric utilities and consumers, there is still lack of effective processes and methodologies to minimize the energy waste in intensive energy sectors such as industrial facilities, commercial buildings, university campuses, etc. Such large sectors are characterized by stranded equipment, which makes monitoring the electric energy consumption at individual equipment level from economic and practical perspectives infeasible. Given this fact, the process of identifying the energy waste in specific equipment, selecting candidate loads for demand side provision, equipment troubleshooting, and finding optimal operating strategies for this equipment considering TOU while ensuring not to compromise the whole sector goals/objectives become challenging to realize. Substantial research effort is needed to achieve a non-intrusive and cost-effective energy consumption monitoring approach that paves the road toward realizing smarter, efficient, and accurate metering infrastructure. The proposed research work aims to bridge the gap between existing energy monitoring system and smart energy consumption through the development of intelligent algorithms for AMI based on single-point sensing and innovative un-supervised learning to breakdown the aggregated energy consumption in the targeted sectors hence better quantify the electricity usage and identify the energy waste. With close co-operation with the industrial partners of this project, the proposed research work will supply Hydro One with the necessary information that will help reducing the impact on their transmission/distribution systems, increase the lifetime/performance of their assets, expand the capabilities of the energy monitoring products of Siemens Canada while ensuring low product cost and more energy consumption information accessibility. Moreover, the proposed research work will also help Ontario Power Generation to reduce their generation production cost and reduce their carbon foot print.