STORM: Self-organising Thermal Operational Resource Management
Click on the here to watch the STORM project video
Duration:
March 2015 to March 2019.
Target Countries:
Belgium, Netherlands, Sweden.
Description / Objectives:
The project tackled energy efficiency at district level by developing an innovative district heating & cooling (DHC) network controller. The project partners developed a controller based on self-learning algorithms, which was experimented in the two STORM demo sites. The developed controller enabled to maximize the use of waste heat and renewable energy sources in DHC networks.
The controller has been implemented in 2 demo sites, Mijnwater BV in Heerlen (NL) and Växjö Energi in Rottne (SE), where the resulting energetic, economic and environmental gains were assessed. Read about the results here.
Through replication, dissemination and education efforts, the project outcomes were transferred to several stakeholders across the EU, and contributed to a wider deployment of DHC networks on EU level.
Check out the STORM events here and watch the training webinars here.
STORM project was coordinated by Energyville, a collaboration between VITO, KU Leuven and IMEC and involves Mijnwater BV, Hogeschool Zuyd, NODA Intelligent Systems, Euroheat & Power and Växjö Energi.
STORM Demo Sites:
To experiment the STORM platform’s general applicability, the controller was 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.
Find out more about the STORM controller here.
Results:
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.
- “Status of the Horizon 2020 Storm Project,” Energy Procedia, Volume 116, June 2017: p.170 – 179.
- “Operational thermal load forecasting in district heating networks using machine learning and expert advice,” Energy and Buildings, Volume 162, March 2018: p.144 – 153.
- “Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods,” Energy, Volume 157, August 2018: p.141 – 149.
- STORM Factsheet.
- Final report on the performance of the STORM controller.
Project Partners:
VITO (BE); Mijnwater BV (NL); Hogeschool Zuyd (NL); NODA Intelligent Systems (SE); Euroheat & Power (BE); Växjö Energi (SE).
Websites:
http://cordis.europa.eu/project/rcn/194614_en.html
Funding:
Horizon 2020 research and innovation programme.