Advanced cluster and predictive analysis tool development for corporate real estate energy usage

The objective of this project is to develop an analytics tool for REALPAC to use to better classify buildings using the “20 x ‘15” dataset collected by REALPAC since 2009. Preliminary analysis has been conducted of this data in past years, but this has been limited to a simple retrospective analysis. The tool that will be developed will incorporate “big data” techniques such as machine learning, which will allow the classification of buildings as “likely strong performers”, “likely poor performers”, “high probability for significant energy conservation”, and “low probability for significant energy conservation”. The intern will undertake data cleaning and classification tasks, as well as the development and testing of the predictive models and associated algorithms that will make up this tool. This tool, in turn, will provide REALPAC with a depth of insight previously unavailable to inform both public policy as well as corporate sustainability strategies of its member organizations. TO BE CONT.

Faculty Supervisor:

Jenn McArthur


Carleen Lawson


Real Property Association of Canada


Architecture and design






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