Smart building data analytics

RealTerm Energy is developing an AI-based solution for predictively optimizing HVAC (heating, ventilation, and air conditioning) systems in smart buildings. A substantial component of this solution is a data analyzer which will mine an enormous dataset to model the operational behaviours of the buildings in different locations around the world. Based on enormous datasets collected from thousands of sensors during past years in several buildings, this project is aimed to extract patterns and build correlations among various metrics in order to model the behaviour of internal building environment. The ultimate goal is to detect shortcomings in current operational plans of the buildings, and then generate control policies in real-time that will minimize energy consumption according to real-time behaviours of each type of buildings, the architecture of the building, its geographical location, as well as outside weather condition

Faculty Supervisor:

Kim Khoa Nguyen


Fatma Mtibaa


RealTerm Energy


Engineering - computer / electrical



École de technologie supérieure



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