Machine Learning Strategies in the Physical North American Power Markets

Machine learning techniques have been applied to the financial industry for some time. They have allowed large utilities and generators to better forecast their needs, and the prices they will pay, leading to a generally more efficient grid. However, very little research has been done that could benefit power marketers, who do not have a load to serve or a generating facility to manage.
The application of machine learning techniques has yielded great results in the financial industry. Due to their capacity to uncover non-linear relationships between large sets of variables, they seem like the perfect tool to better understand the electric grid and uncover trading strategies based on these discoveries.

Faculty Supervisor:

Frédéric Godin;Manuel Morales;Fabian Bastin;Ivan Contreras

Student:

Partner:

Plant-E Corp

Discipline:

Mathematics

Sector:

Utilities

University:

Concordia University; Université de Montréal

Program: