Project showcase

GBTN32 – IoT and AI for a Smart Sustainable Building

Project Typology
Project Innovation
1. Increase energy efficiency of Alternate Energy Lab building at Red Deer Polytechnic by moving to demand based control with a novel approach. 2. Demonstrate a practical approach in reducing 40% energy usage and 25% reduction in natural gas totaling 10.5 metric tons of CO2 equivalent 3. Scale this approach to other building across Red Deer Polytechnic as a part of “living lab” and in Central Alberta (hospitals, library , gyms, etc). 4. Educate building operators, researchers and industries to adopt this approach using our Data Sharing Alliance cloud platform.
Project Partners
University of Alberta, Johnson Controls, BGIS, Sol Invictus
Project Location
Red Deer
Project Lead
Red Deer Polytechnic
Building Owner
Red Deer Polytechnic
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Deployment of low cost IIoT sensors to measure air quality and occupancy inside Red Deer Polytechnic’s Alternative Energy Lab (AEL) building for optimizing HVAC use. The technology is vendor agnostic and use advanced data analytics model (including AI) to efficiently control (real-time) HVAC unit based on zonal air quality and occupancy. Minimal intervention and HVAC retrofitting is required which makes this approach economically feasible.

Media CoveraGe and website links

By integrating IoT-based CO₂ and occupancy sensing with advanced data analytics and machine learning, this novel Living Lab model at Red Deer Polytechnic is pioneering real-time optimization of HVAC performance. Built entirely on open-source platforms within our Alternative Energy Lab, it promotes transparency, replicability, and sector-wide collaboration, ultimately advancing the development of smart, efficient, and adaptive building systems.

– Debjyoti Sen (Red Deer Polytechnic)