Uptime Energy Control Procedure Using Machine Learning

In a time where energy use awareness is more and more prevalent due to its significance in global warming, all sectors of society are putting effort to participate in finding new ways to reduce energy consumption. The industrial sector in Canada consumes nearly 1/3 of total energy. A challenge for industrial organizations is to define improvement actions that will significantly reduce the energy consumption of their production facilities, resulting in significant energy savings, while maintaining an adequate level of production for its customers. However, in industrial processes, there are complex dynamics between energy and a large number of process variables that affect energy consumption. Recent advances in machine learning combined with the vast availability of process data thus make process industries ideal candidates for energy performance optimization. TO BE CONT’D

Faculty Supervisor:

François Bouffard

Student:

Partner:

Energy Performance Services (EPS) Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

McGill University

Program: