Key Takeaways

• University of Alberta is developing a tool to optimize wind flow, leading to significant energy savings and improved urban air quality.
• A new open-source machine learning tool is being tested at Red Deer Polytechnic to automate wind flow mapping, promising faster results.
• The initiative aligns with U of A’s sustainability goals and includes a broader effort to integrate solar energy solutions on campus.

Innovative Wind Flow Optimization at University of Alberta

A professor at the University of Alberta (U of A), Brian Fleck, is spearheading a project aimed at revolutionizing wind flow understanding in urban settings. This initiative promises to enhance air quality, improve energy efficiency, and identify renewable energy hotspots. Historically, building technicians have operated heating, ventilation, and air conditioning (HVAC) systems at full capacity irrespective of actual wind conditions, resulting in unnecessary energy costs. Fleck emphasizes that optimizing these systems could save institutions like U of A significant amounts on heating costs.

The project began with the objective of creating a detailed airflow map over U of A buildings. Fleck noted that previous traditional models were inefficient, requiring students to manually digitize incompatible data. The innovative solution is an open-source machine learning tool designed for automated wind flow mapping, which is currently being tested at Red Deer Polytechnic with real-time wind data from campus anemometers.

Fleck expects this new approach will dramatically reduce the time needed to wind map U of A, thanks to the collaboration with Red Deer Polytechnic. His company, Flexible Machines Corp., is working to commercialize the software, which has received additional support from Alberta Innovates and the City of Edmonton. By enhancing urban energy modeling systems, the automated mapping tool could fundamentally change how small-scale wind and ventilation systems are deployed.

Michael Versteege, U of A’s director of Energy and Climate Action, highlighted the university’s larger strategy of utilizing its campuses as a “living lab” for innovative solutions to climate change. Along with Fleck’s work, researchers—including Mustafa Gül—are using AI to plan optimal solar panel placements on campus buildings. This effort underscores a comprehensive strategy to integrate both wind and solar energy modeling, informing a detailed 3D model of the campus infrastructure.

Currently, the university has about two million watts of solar photovoltaic capacity, enough to power approximately 400 homes. The set goal is to increase this capacity to six to seven megawatts, maximizing energy savings during peak times while reducing greenhouse gas emissions. This comprehensive approach exemplifies how ongoing projects can lead to substantial benefits in climate change mitigation and energy efficiency, reflecting the U of A’s commitment to sustainability.

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