Data-Driven Disaggregation of HP Loads in Transmission Systems with Limited Data

This project develops AI- and data-driven methods to improve visibility of heat pump (HP) loads in power grids. By disaggregating HP consumption from aggregate smart meter and grid data, it will support accurate demand forecasting, reliable grid operation, and planning for heating electrification in cold climates, contributing to reduced greenhouse gas emissions.

Faculty Supervisor:

Claudio Adrián Cañizares;Maurice Dusseault

Student:

Partner:

Karlsruher Institut für Technologie

Discipline:

Engineering

Sector:

Education

University:

University of Waterloo

Program:

Globalink Research Award

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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.