STORM: Self-organising Thermal Operational Resource Management
Click on the here to watch the STORM project video
March 2015 to August 2018.
Description / Objectives:
The project tackles energy efficiency at district level by developing an innovative district heating & cooling (DHC) network controller. The project partners have developed a controller based on self-learning algorithms, which is currently experimented in the two STORM demo sites. The developed controller will enable to maximize the use of waste heat and renewable energy sources in DHC networks.
Through replication, dissemination and education efforts, the project outcomes will be transferred to several stakeholders across the EU, and will thus contribute to a wider deployment of DHC networks on EU level.
- Boosting energy efficiency at district level through the use of waste heat, renewable energy sources and storage systems.
- Building on state of the art technical developments and advanced business models.
- Developing an innovative controller for district heating & cooling (DHC) networks.
- Demonstrating the benefits of smart control systems.
- Quantifying the energetic, economic and environmental benefits of the controller.
- Developing innovative business models needed for the large-scale roll-out of the controller at reduced costs.
- Designing a scalable and performing self-learning control approach requiring limited external experts.
- Increasing awareness on the need to control DHC networks in a smart way.
STORM Demo Sites:
To experiment the STORM platform’s general applicability, the controller will be demonstrated in two existing demo sites: one highly innovative low-temperature DHC network in the Netherlands and a more common medium-temperature district heating grid in Sweden. In Rottne, Sweden, the STORM controller will help to reduce the oil usage and optimise the bio-fuel boilers. For Heerlen, the Netherlands, the STORM controller is vital to manage the capacity of the flooded mine galleries and to balance the different clusters.
The following publication have been produced by the project partners:
- “Operational Demand Forecasting in District Heating Systems Using Ensembles of Online Machine Learning Algorithms,” Energy Procedia, Volume 116, June 2017: p.208 – 216.
Horizon 2020 research and innovation programme