Data-driven smart buildings: data sandbox and competition
The main aim of this project is to develop Machine Learning and smart control algorithms that will enable or improve new data services for the building sector. The developed algorithms extrapolate as much information as possible from the existing energy metering data, to avoid costly installation of sub-metering in existing buildings.
Commercial and public buildings often already have equipment and control capability in place for delivering flexible demand into the grid. Consequently, buildings offer the potential to be one of the lowest-cost opportunities for providing the flexible demand needed to support increasing levels of variable renewable energy resources in electricity grids. Unfortunately, it has proven difficult to activate and scale this latent flexible-demand opportunity. New data-driven software services are needed, harnessing the power of machine learning, to deliver smart-grid-enabled buildings at scale.
ADRENALIN three objectives are:
- Objective 1: Gather a large pool of data to allow testing the scalability and reusability of the proposed algorithms.
- Objective 2: Organize competitions as a means of crowdsourcing the solutions to the data challenges.
- Objective 3: Implement the best solutions on different digital platforms, in real-life applications.
Key figures
Participants
| Partner | Subsidy | Auto financing |
|---|---|---|
| Syddansk Universitet | 2,51 mio. DKK | 0,28 mio. DKK |
| ReMoni ApS | 0,90 mio. DKK | 0,60 mio. DKK |
Contact
Campusvej 55,
5230 Odense M
Tlf.: 29125778
