Subventions et des contributions :

Titre :
Computational Algorithms for Energy Efficiency and Cost Reduction
Numéro de l’entente :
EGP
Valeur d'entente :
25 000,00 $
Date d'entente :
14 juin 2017 -
Organisation :
Conseil de recherches en sciences naturelles et en génie du Canada
Location :
Ontario, Autre, CA
Numéro de référence :
GC-2017-Q1-00386
Type d'entente :
subvention
Type de rapport :
Subventions et des contributions
Informations supplémentaires :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019)

Nom légal du bénéficiaire :
Feng, Wenying (Trent University)
Programme :
Subventions d'engagement partenarial pour les universités
But du programme :

Energy efficiency affects people's daily life, industry and social environment. For example, properly shiftingx000D
electricity usage can significantly reduce its cost and the demand during periods of limited supply. As a leadingx000D
company in the market of energy efficiency, Lowfoot Inc. has been developing innovative tools for energy costx000D
reduction by Artificial Intelligence approaches.x000D
Supported by Lowfoot Inc., the project will introduce computational algorithms that are efficient in predictionx000D
of electricity usage and provide recommendations to minimize user's energy cost. New advancement inx000D
Machine Learning, big data and related areas will be investigated for effective solutions. With theoreticalx000D
foundation in mathematical modeling and computing techniques, deep learning and cloud computing will bex000D
applied. The objective is to improve the forecast accuracy of the system.x000D
The work will contribute directly to today's fast moving areas of data analytics by deep artificial intelligence ofx000D
which Canada is at the forefront. The results will benefit consumers and small business on energy managementx000D
and have the potential of creating new employment for the Canadian society.