Development of the energy efficiency algorithm

Sustainable access to energy requires a shift from carbon-intensive sources. Hence, this project aims to improve efficiency of national energy systems via informed decision-making. The holistic modelling framework will integrate individual aspects of energy production and consumption in buildings. Our platform will simulate and optimize the energy source composition to minimize emissions and economic impact. We will employ data preparation and development of a machine learning model to evaluate energy efficiency opportunities in buildings. The data will be gathered from public sources, from available APIs, and potentially utility companies to be usable in a machine learning model. This machine learning model will use weather, location, energy information, and appliances’ characteristics to evaluate the energy performance of a building in a portfolio. This can allow Canadian users to understand energy efficiency opportunities, lowering their carbon footprint and to expand the capability of energy audits in public and private sectors.

Faculty Supervisor:

Sergey Ishutov;Nasim Hajari

Student:

Partner:

Mitsidi Solutions Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Concordia University of Edmonton

Program: