Improving Human-centric Facility Management through Machine Learning Analysis and Visualization

Buildings represent up to 40% of primary energy consumption. To optimize that energy cost vs. the comfort of its occupants, Facility Management (FM) relies on data from sensors, and on automation, to increase efficiency. The majority of existing buildings however have limited automation, so it is up to Facility Managers to interpret and act upon the information resulting from the various building sensors. This is often difficult without the appropriate contextual information to guide and support decisions. This project aims at addressing this issue by using Machine Learning methods applied to FM data, and make the results more explicit for human users, by providing better informational context as well as the development and application of new data visualization techniques, and improve Facility Managers’ decision-making.

Faculty Supervisor:

Fred Popowich


Shanghao Chen


CopperTree Analytics


Computer science


Information and communications technologies




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