Reference number : GC-2017-Q1-00386, amendment number : , agreement type : G, recipient legal name : Feng, Wenying (Trent 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 : Energy efficiency affects people's daily life, industry and social environment. For example, properly shifting_x000D_
electricity usage can significantly reduce its cost and the demand during periods of limited supply. As a leading_x000D_
company in the market of energy efficiency, Lowfoot Inc. has been developing innovative tools for energy cost_x000D_
reduction by Artificial Intelligence approaches._x000D_
Supported by Lowfoot Inc., the project will introduce computational algorithms that are efficient in prediction_x000D_
of electricity usage and provide recommendations to minimize user's energy cost. New advancement in_x000D_
Machine Learning, big data and related areas will be investigated for effective solutions. With theoretical_x000D_
foundation in mathematical modeling and computing techniques, deep learning and cloud computing will be_x000D_
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 of_x000D_
which Canada is at the forefront. The results will benefit consumers and small business on energy management_x000D_
and have the potential of creating new employment for the Canadian society., program purpose french : Energy efficiency affects people's daily life, industry and social environment. For example, properly shifting_x000D_
electricity usage can significantly reduce its cost and the demand during periods of limited supply. As a leading_x000D_
company in the market of energy efficiency, Lowfoot Inc. has been developing innovative tools for energy cost_x000D_
reduction by Artificial Intelligence approaches._x000D_
Supported by Lowfoot Inc., the project will introduce computational algorithms that are efficient in prediction_x000D_
of electricity usage and provide recommendations to minimize user's energy cost. New advancement in_x000D_
Machine Learning, big data and related areas will be investigated for effective solutions. With theoretical_x000D_
foundation in mathematical modeling and computing techniques, deep learning and cloud computing will be_x000D_
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 of_x000D_
which Canada is at the forefront. The results will benefit consumers and small business on energy management_x000D_
and have the potential of creating new employment for the Canadian society., agreement title english : Computational Algorithms for Energy Efficiency and Cost Reduction, agreement title french : Computational Algorithms for Energy Efficiency and Cost Reduction, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-06-14, 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