Self-Learning, Smart Heating Digital Twins on the Edge
Problem:
Existing real estate consumes 40% of our energy. Traditional efficiency strategies are too complex and costly. By using self-learning algorithms with edge-native actuation strategies, organizations can now deploy sensors and predict future performance. They can create a baseline for any asset, and automatically actuate what’s needed to reduce a building's carbon footprint.
Solution:
The use case delivers a smart Building Information Modeling (BIM) to edge-native digital twin strategy. It includes BIM models, wireless cabling, sensors, and actuators that remotely control radiators to improve indoor air quality, efficient heating distribution, and reduce the carbon footprint for assets integrated with game-engine tech.
Digital twins provide an immersive real-time collaboration arena, making it easier for stakeholders to understand reality from different perspectives. This enables people, systems, and AI actors to natively transfer knowledge, while providing simulation capabilities based on real-time reality emulation.
Outcomes:
This use case approach yields 25% energy savings, reduces maintenance costs, and increases well-being and productivity. This results in a higher net operating profit and a foundation to make all future decisions. Utilizing standardized data, tagging standards, and self-learning AI on the edge can create Digital DNAs of any asset, teaching other systems what they need to be aware of. Organizations can quickly show value by simulating existing configurations and potential futures in ways that are easy to understand.
Key Players:
The WINNIIO BIM-to-twin strategic roadmapping is at the heart of the smart heating use case. WINNIO provides advisory services based on its experience in construction, 3D collaboration, and digital twins. The solution's visualization technology enables decision-making, first response reaction, and real-time collaboration.