Energy reduction in HVAC systems in a commercial building environment using data-driven approaches

The main goal of this project is to develop data-driven approaches to reduce energy consumption and cost when operating commercial building’s cooling systems. Indeed, according to recent studies the building sector is one of the largest energy-consuming entities (almost 40% of global energy consumption) and this consumption is predicted to increase by 50% by 2050. Thus, there is an urgent need to provide solutions to reduce energy consumption taking into account the importance to improve environmental sustainability and the increase of electricity prices. HVAC (Heating, Ventilation, and air-conditioning) systems dominate energy usage in commercial buildings (between 40 and 70% of the total electricity consumption). In this project, we propose to tackle several challenges to improve Brainbox solutions related to HVAC systems.

Faculty Supervisor:

Nizar Bouguila


Fatma Najar


BrainBox AI


Engineering - computer / electrical


Information and communications technologies


Concordia University



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