An AI-driven toolchain to support a cost-effective green transition

The HeatCheck project
HeatCheck aims to develop a scalable, commercial-grade, open-source, AI-driven toolchain that merges meter data with ubiquitous wireless heat cost allocator data to optimize heating systems in apartment buildings. District heating operators have focused on supply temperatures and flows to satisfy various demand profiles. However, they typically overlook end-users and substations as beyond their control. Thus, buildings are bottlenecks in the transition to low-temperature district heating.
The HeatCheck toolchain empowers building owners to operate apartment buildings with unprecedented efficiency, in turn, paving the path for low-risk, near-term investments in renewable energy sources and large-scale investments in heat pumps in district heating networks. Ista and Techem controls roughly two-thirds of the Danish market for allocating heat costs in apartments, and combined provide heat metering in 25 million apartments globally.
HeatCheck seeks to leverage that massive data to detect and diagnose faulty heating systems automatically, optimize central controls to constrain heat losses and minimize harm from misuse, improve service offerings using digital-twin-based analytics and actively shift peak loads. The HeatCheck project provides a unique opportunity for heat cost allocation companies, district heating companies, building operators and research institutions to develop new services generating novel revenue streams and ensuring a cost-effective green transition.
Partners
HeatCheck's value chain
Project structure
Heatcheck is structured around 6 workpackages:
WP1: Project management
Coordination of meetings (e.g. steering committee meetings, work package leader meetings, and general assemblies), reporting to IFD and communicating internally.
WP2: Benchmarking
WP2 establishes measurements, baselines and benchmarks of the case buildings. Optimal heating temperatures from preliminary algorithms are implemented and demonstrated
WP3: Data Infrastructure
WP4: AI-driven Heating Control
WP4 builds upon cyber-physical models - combining properties of heating system with data – and fault detection and diagnostics algorithms to improve control, minimize operation temperatures and constrain heat losses. Cyber-physical models offers digital-twin-based analytics, indicating the impact of small cost- effective physical alterations to reduce critical bottlenecks, such as replacing a specific radiator or installing a pressure control valve. This task will also indicate the energy flexibility potential from implementing a ‘smart controller’, capable of reducing supply temperatures below the calculated minimum during peak periods while increase the setpoints during off-peak periods
WP5: Users, Value and Market
WP5 has the concrete tasks of engaging building personnel in the development of the HeatCheck toolchain and documenting HeatCheck’s realized and expected economic impact on stakeholders based on results from case buildings, and innovating business models and go-to-market strategies to leverage the toolchain’s added value (T11)
WP6: Communication
This WP coordinates publications in scientific journals and industry media and establishes a process for public releases of the HeatCheck toolchain. It publishes ‘success stories’ from the case buildings.
Outreach
Appendix 3
HEATCHECK Dissemination Activities 01.05.23-31.12.23
Journal papers
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Conference contributions
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Workshops
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Press
Gridtech.dk article: Varmeudnyttelsen skal højnes i etageejendomme, så temperaturen kan bankes ned - Nyt AI-projekt viser vejen
Presentations
Oplæg ved IEA DHC Annex Definition Workshop TS9, Berlin, 22 November 2023 (Christian Holm Christiansen/Jakob Fester)
Master theses
Cesar Delafargue - Optimal control of space heating using existing data sources – July 2023
SoMe
LinkedIn: https://www.linkedin.com/feed/update/urn:li:activity:7131214043800858624/
LinkedIn: https://www.linkedin.com/feed/update/urn:li:activity:7131286099989057536/
HEATCHECK Dissemination Activities 01.01.24-31.12.24
Presentations
Digital Twins of Hot Water Installations – Kevin Smith - Danmarks Almene Boliger Varmemålerdag - 7. feb 2024
Presentation (Christian Holm Christiansen) ved temadag “Data til den grønne omstilling i bygninger”, 21. marts 2024, Teknologisk Institut, Aarhus.
Presentation (short mentioning of HeatCheck) - Christian Holm Christiansen – TS9 IEA DHC Stockholm Nov 14-15 2024
Journal papers
Yang, Q., Salenbien, R., Smith, K. M., & Tunzi, M. (2024). Identifying untraced faults associated with high return temperatures from heating systems in buildings connected to district heating networks. Energy, 309, 133097. https://doi.org/10.1016/j.energy.2024.133097
Tunzi, M., Yang, Q., Olesen, J. B., Diget, T., & Fournel, L. C. (2024). Economic viability and scalability of a novel domestic hot water substation for 4th generation district heating: A case study of temperature optimization in the Viborg district heating network. Energy, 313, Article 134010. https://doi.org/10.1016/j.energy.2024.134010
Conference contributions
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Workshops
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Master theses
Johannes Wildfeuer: Continuous commissioning of hot water installations using a digital twin, Feb 2024
Marianna Baranska: Developing Sustainable Business Models for Data-Driven Optimal Heating in Danish Apartments, July 2024
Zakaria Ali: Grey-box modeling for identification of thermal dynamics of smart buildings, June 2024
SoMe
Interreg-ØKS: Grøn Omstilling i Etageboliger. Map potential for low-temperature district heating in apartment buildings using data. Exploring technical pilots of heating station installations to address bottlenecks. Coordinator Gate21. https://gate21.dk/projekt/groen-omstilling-i-etageboliger/
Forsyningsdigitaliseringprogrammet. Integrating data from utilities. Operated by Danish Energy Agency. https://www.forsyningsdigitaliseringsprogram.dk/
Deliverables
Deliverable 2: Full documentation on access, handling, privacy on CenterDenmark's data platform
Click here to see the full document
Contact
Christian Anker Hviid Associate Professor cahv@dtu.dk