Case Study: Ryerson University (Toronto Metropolitan University) - Smart Campus

FuseForward partnered with Ryerson University (Toronto Metropolitan University) to support their smart campus initiative with advanced data services & analytics expertise.


Enabling the Toronto Metropolitan University (formerly Ryerson University) Smart Campus

Smart building technology has the potential to optimize energy consumption, improve occupant comfort, lower costs and improve emergency response.

Ryerson University’s (Toronto Metropolitan University) Smart Building Analytics group, led by Associate Professor Jenn McArthur, explores how utilities, transportation, building use, and human behavior can be optimized through insights generated by streaming data.

“In my work, I look at how to use data to improve the performance of the built environment,” explains Jenn MacArthur, Associate Professor at Ryerson University (Toronto Metropolitan University).


Optimal work environments have social, medical and environmental benefits. How can we make spaces healthier for people? How can we make an office into a great work environment?

Jenn MacArthur, Associate Professor


A major Ryerson University project is looking to create a single platform integration between building, infrastructure, location, and transportation data, with the aim of creating a “digital twin” replica of the campus, which will ultimately serve as a small-scale model of a smart city.

Ryerson (Toronto Metropolitan University) is a member of FuseForward’s Intelligent Systems Alliance, a network of academic and industry partners that explores the application of advanced analytics to concrete, real-world needs. Based on this common aim and to support their needs with regard to data services and modeling, Ryerson chose to collaborate with FuseForward as a strategic partner.

Supporting smart building research with secure data services

Large complexes such as university campuses can generate millions of data points per day from HVAC systems, sensors, energy and water meters, Wi-Fi nodes, and IoT devices.

An initial Ryerson (Toronto Metropolitan) test system streamed data from SCADA systems to hard drives, which was slow and vulnerable to outages. The first step was therefore to improve scalability, streaming data retention, compute capacity, and time-to-access for researchers and, ultimately, building managers.

To achieve this, FuseForward worked with Ryerson (Toronto Metropolitan) to structure, normalize and stream that data into a secure cloud environment managed by FuseForward, that incorporates Amazon Web Services (AWS) Data Services.

One of our challenges is that building data is really varied. There’s different kinds of data from different kinds of systems and formats, and we’ve got to bring them all into one place.

Jenn MacArthur, Associate Professor


As a result, machine learning tasks that previously took up to eight days are now completed in a matter of hours. The system is able to ingest 8.4 million records per day, while data retention has increased tenfold.

To structure and categorize data produced by buildings in the university environment, FuseForward drew on 30 years of experience in data modeling for complex systems to create a smart campus-specific ontology model. The ontology provides clear identification of data points and enables researchers to run queries regarding specific pieces of equipment or spaces.

Informing real-world action with digital twins

Two years into the project, datasets are being leveraged into practical applications. Ryerson (Toronto Metropolitan) is currently working on an interactive data visualization platform that will enable staff to view data trends for equipment or faculty rooms.

“You can look at any given space and see any complaints alongside your current data stream,” Jenn says, “For example, temperature sensors in a room where people are complaining about feeling too cold. By bringing everything into one common environment, you can both troubleshoot and make knowledge-based decisions.”

A digital twin, or digital replica, acts as a visual representation of structured data from a building or a physical asset. Data modeling for the 19-storey Daphne Cockwell Complex, which hosts student residences on top of the university’s Health Sciences faculty, has enabled Ryerson to complete a full digital twin of the building.

The recently-built complex hosts over 10,000 building automation system (BAS) points which, via its digital twin, provide an in-depth look into the building’s energy consumption, environmental performance, airflow, and potential congestion in a single interface.


digital twin

Embedded with IoT devices during construction, the Daphne Cockwell Complex was the first candidate for virtual simulation. 

Using streaming data, the digital twin of the Daphne Cockwell Complex provides facility managers with a real-time view of building operations.

Digital twins provide building managers with a real-time overview of equipment and spaces, but also enable them to simulate events and scenarios, such as fire drills or the potential impact of shutting down certain utilities during vacations, before taking real-world action.

FuseForward provided the necessary cloud analytics expertise and tools to manage and analyze the massive amount of data coming in from the real buildings and simulations in the digital twin.

At this point, digital twins for the other campus buildings are restricted to space management information as Ryerson works towards creating a full digital replica of the entire campus.

“We’re in the process of populating those replicas with data. All data will be tagged consistently with the ontology developed with FuseForward, which is going to make it so much easier for us to bring it inbound and online,” Jenn says. “A digital twin is the data behind it. If you don’t actually have that, it’s just pictures, rather than something that’s truly ‘smart’.”



Results of our work

Ingestion and storage of up to 8.4 million records per day


calculation engine

Tenfold increase in streaming data retention, from 10 to 100%


Highly secure data storage and analytics environment based in Canada


Smart campus-specific ontology model that structures data


Optimizing building systems through predictive analytics

The next steps of the project will involve using machine learning to identify usage patterns, detect anomalies and optimize the campus environment. “We’re starting to look at building event detection algorithms to detect system points that aren’t behaving as they should be,” Jenn explains, “We should get to the point where we’re able to predict when a boiler will need maintenance, and if it’s worth doing it straight away or waiting until a vacation shutdown. Being able to predict faults before they’re visible can help prevent serious damage costing thousands of dollars.”


Next steps: replicating our success across other industries

Ryerson University (Toronto Metropolitan) and FuseForward will expand this research through the Smart Campus Integration & Testing (SCIT) Hub. This new facility will provide researchers with a realistic environment in which to experiment with smart building technology and look at how IoT devices can work as part of integrated systems without impacting residents. The lab will comprise a fully equipped remote operations centre connected to building systems throughout the campus.

Data-generated insights have the ability to optimize not only campus environments, but also asset-intensive organizations, such as hospitals, industrial plants, cities, and transportation agencies among many others. FuseForward and Ryerson (Toronto Metropolitan) look forward to continuing to collaborate over the years to come.


I hope this will become a 20-year partnership. I’m looking forward to seeing how far we can push up.

Jenn MacArthur, Associate Professor


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