Machine Learning and Data Mining Approaches for Smart Buildings

The goal of this project is to develop machine learning and data mining algorithms relying on non-intrusive common sensor data to estimate and predict smart buildings’ occupancy and activities. Efficient feedbacks are automatically supplied to the end user to involve occupants and increase their awareness about energy systems. This consists of generating reports helping the occupant to understand his/her energy management system and thus to be involved in the decision-making process. The project directly aligns with Ericsson’s IoT mission and expands its operator potential opportunities by exploring a dimensionality in real-time automation, monitoring and tracking, and smart surveillance. It also further strengthens Ericsson’s position in the IoT market which has a projected additional revenue potential of up to 36% (USD 619 billion) by 2026. Moreover, the machine learning techniques to be developed can be easily adapted to other problems of interest to Ericsson.

Faculty Supervisor:

Nizar Bouguila


Samr Ali;Manar Amayri


Ericsson Canada


Engineering - computer / electrical


Information and communications technologies


Concordia University



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