HEAT 4

CONTENT FROM THE HEAT 4.0 PROJECT

HEAT 4.0 was an interdisciplinary innovation project supported by the Innovation Fund Denmark (IFD) with 3.4 Mio. €. The project aimed at creating the next-generation digital platform for the district heating sector.

DTU Compute, DTU Management, and DTU Construct/DTU Sustain (formerly DTU Byg) were three of the 16 partners representing the whole value chain of the district heating sector.

The IFD project was carried out between 2019 to 2022, whereafter the HEAT 4.0 consortium is maintained as an open consortium, where interested district heating companies from Denmark can contact any partner to get a single point of contact. For international district heating companies, NIRAS is the first contact point. 

Learn more on HEATman.dk/

PARTNERS and DEMO PROJECTS

 

HEAT 4.0 aims at supporting the digitalization of the district heating sector as a whole.

This includes several innovative developments and novel solutions that had to be examined in real-case setups.
For this purpose, 3 district heating companies with different setups, joined the consortium.
These cases are described in the current section. The three cases are (links go to HEATman.dk):

The first and last DH operators produce their heat themselves, while Trefor is buying their heat from a local provider. Hence solutions are adjusted to the individual case, enabling the consortium to develop generic solutions that will meet many of the existing national and international cases.

As the developments and testing at the above three cases were successfully finalized, the first commercial installations of HEAT 4.0 solutions are listed for other cases and cooperative activities involving HEAT 4.0 partners and relevant to the current consortium. Other district heating innovations were run by HEAT 4.0 partners but not financed directly under this current project – you find some of them here on the CITIES’ homepage 

Find the final reports from HEAT 4.0 on heatman.dk 

SCIENTIFIC PUBLICATIONS

  • Amato, V., Broholt, T. H., Christensen, L. R. L., Hedegaard, R. E., Knudsen, M. D., & Petersen, S. (2022). Laboratory test of commercial smart radiator thermostats when used for load shifting. CLIMA 2022 Conference. https://doi.org/10.34641/clima.2022.163
  • Amato, V., Knudsen, M. D., & Petersen, S. (2020). Data-based calibration of physics-based thermal models of single family houses. In L. Georges, M. Haase, V. novakovic, & P. G. Schild (Eds.), BuildSIM-Nordic 2020 Selected papers (Issue 5, pp. 285–292). SINTEF Academic Press.
  • Andersen, K. S., Wiese, C., Petrovic, S., & McKenna, R. (2020). Exploring the role of households’ hurdle rates and demand elasticities in meeting Danish energy-savings target. Energy Policy, 146, 111785. https://doi.org/10.1016/j.enpol.2020.111785
  • Bergsteinsson, H. G., Møller, J. K., Nystrup, P., Pálsson, Ó. P., Guericke, D., & Madsen, H. (2021). Heat load forecasting using adaptive temporal hierarchies. Applied Energy, 292, 116872. https://doi.org/10.1016/j.apenergy.2021.116872
  • Bergsteinsson, H. G., Nielsen, T. S., Møller, J. K., Amer, S. B., Dominković, D. F., & Madsen, H. (2021). Use of smart meters as feedback for district heating temperature control. Energy Reports, 7, 213–221. https://doi.org/10.1016/j.egyr.2021.08.153
  • Bergsteinsson, H. G., Sara, B. A., Per, S. N., & Henrik, M. (2021). Digitalization of District Heating (p. 45).
  • Bergsteinsson, H., Madsen, H., & et al. (n.d.). Estimating Temperatures In a District Heating Network Using Smart Meter Data. Energy Conversion and Management.
  • Bergsteinsson, H., Møller, J. K., & Madsen, H. (2020). Quantification of heat demand forecast accuracy improvements by localized weather forecast. 2nd ISEC, Graz, Austria.
  • Bergsteinsson, H., Møller, J. K., Thilker, Christian Ankerstjerne, C. A., Guericke, D., Heller, A., Nielsen, S. T., & Madsen, H. (n.d.). Data-Driven Methods for Efficient Operation of District Heating Systems. Springer Books 2022.
  • Bergsteinsson, H., Nielsen, T. S., Møller, J. K., Amer, S. B., Dominković, D. F., & Madsen, H. (n.d.). Estimate forward temperatures for temperature control. Energy and Buildings.
  • Biemann, M., Scheller, F., Liu, X., & Huang, L. (2021). Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control. Applied Energy, 298, 117164. https://doi.org/10.1016/j.apenergy.2021.117164
  • Bilal Muhammad, S., Nielsen, P. S., & Keles, D. (2022). Decarbonization pathways for District Heating sector; A Danish case study. 43rd  IAEE International Conference on Energy Economics, Tokyo, Japan.
  • Blanco, I., Andersen, A. N., Guericke, D., & Madsen, H. (2020). A novel bidding method for combined heat and power units in district heating systems. Energy Systems, 11(4), 1137–1156. https://doi.org/10.1007/s12667-019-00352-0
  • Broholt, T. H. (n.d.). A mixed-methods study on resident thermal comfort and attitude towards peak shifting of space heating. Energy Research and Social Science.
  • Broholt, T. H., Christensen, L. R. L., Knudsen, M. D., Hedegaard, R. E., & Petersen, S. (2020). The effect of seasonal weather changes on the performance of databased models of the thermodynamic behaviour of buildings. E3S Web of Conferences, 172, 02005. https://doi.org/10.1051/e3sconf/202017202005
  • Broholt, T. H., Christensen, L. R. L., & Petersen, S. (2022). Effect of measurement resolution on data-based models of thermodynamic behaviour of buildings. CLIMA 2022 Conference. https://doi.org/10.34641/clima.2022.196
  • Broholt, T. H., & Petersen, S. (2022a). Industry survey on demand response from residential space heating as an action to challenges in district heating systems. Energy.
  • Broholt, T. H., & Petersen, S. (2022b). ’Opportunities and barriers for temporal demand response as an action to challenges in district heating’. Energy.
  • Broholt, T. H., & Petersen, S. (2022c). System Identification of high-performing black and grey-box models of thermal building behaviour. Building Simulation.
  • Broholt, T. H., & Petersen, S. (2022d). The robustness of black and grey-box models of thermal building behaviour against weather changes. Energy and Buildings.
  • Christensen, L., Broholt, T. H., & Petersen, S. (2022). Are bedroom air temperatures affected by temperature boosts in adjacent rooms? CLIMA 2022 Conference. https://doi.org/10.34641/clima.2022.38
  • Christensen, L. R. L., Broholt, T. H., Knudsen, M. D., Hedegaard, R. E., & Petersen, S. (2020). The influence of unmeasured occupancy disturbances on the performance of black-box thermal building models. E3S Web of Conferences, 172, 02010. https://doi.org/10.1051/e3sconf/202017202010
  • Dai, W., Liu, X., Heller, A., & Nielsen, P. S. (2022). Smart Meter Data Anomaly Detection Using Variational Recurrent Autoencoders with Attention. International Conference on Intelligent Technologies and Applications, 311–324. https://doi.org/10.48550/arXiv.2206.07519
  • Dai, W., Liu, X., & Nielsen, P. S. (2022). Online Anomaly Detection with a Lightweight IIoT Data Stream Processing Framework. In Submission to IEEE Transactions on Multimedia.
  • Fang, H. (2015). Managing data lakes in big data era: What’s a data lake and why has it became popular in data management ecosystem. 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 820–824. https://doi.org/10.1109/CYBER.2015.7288049
  • Fjerbæk, E. V. (n.d.). Coupling Modelica simulations and a Common Data Environment for BIM. (Draft – to Be Decided).
  • Fjerbæk, E. V., Seidenschnur, M., Kücükavci, A., Smith, K. M., & Hviid, C. A. (2022). From BIM databases to Modelica—Automated simulations of heating systems. CLIMA 2022 Conference. https://doi.org/10.34641/clima.2022.365
  • Freeman, M. R., Nesje, F., Sneum, D. M., & Soysal, E. R. (2021). Discounting and the Green Transition: District Heating in Denmark. In Energy Regulation in the Green Transition (Vol. 1, pp. 98–112). https://forsyningstilsynet.dk/aktuelt/publikationer/danish-utility-regulators-anthology-project-series-on-better-regulation-in-the-energy-sector/vol-1-energy-regulation-in-the-green-transition
  • Gaballo Francesco, H. A., Siddique Bilal Muhammad, Nielsen Per Sieverts. (2022). The role of district heating in the future European energy system. 2022 IEEE International Conference on Power and Energy (PECon).
  • Guericke, D. (n.d.). Economic analysis of network and building flexibility in district heating systems.
  • Guericke, D., Blanco, I., Morales, J. M., & Madsen, H. (2020). A two-phase stochastic programming approach to biomass supply planning for combined heat and power plants. OR Spectrum, 42(4), 863–900. https://doi.org/10.1007/s00291-020-00593-x
  • Guericke, D., & Madsen, H. (2021). A stochastic program for biomass contract selection under demand uncertainty. Energy Systems. https://doi.org/10.1007/s12667-021-00455-7
  • Guericke, D., Schledorn, A., & Madsen, H. (2021). A generic stochastic network-flow formulation for production optimization of district heating systems. International Conference on Operations Research, Bern, Switzerland.
  • Guericke, D., Schledorn, A., & Madsen, H. (2023). Optimization of heat production for electricity market participation. In Handbook of Low Temperature District Heating Ed. By Roberto Garay (pp. 89–105). Springer Books 2022.
  • Hai, R., Geisler, S., & Quix, C. (2016). Constance: An Intelligent Data Lake System. Proceedings of the 2016 International Conference on Management of Data, 2097–2100. https://doi.org/10.1145/2882903.2899389
  • Hedegaard, R., Kristensen, M. H., & Petersen, S. (2020a). Investigating the impact of construction year on the urban-scale energy flexibility potential of single-family houses. 2nd IBPSA-Scotland uSIM conference, Edinburgh, Scotland.
  • Hedegaard, R., Kristensen, M., & Petersen, S. (2020b). Experimental validation of a model-based method for separating the space heating and domestic hot water components from smart-meter consumption data. E3S Web of Conferences, 172, 12001. https://doi.org/10.1051/e3sconf/202017212001
  • Hedegaard, Rasmus Elbæk, R., Friederichsen, L., Tougaard, J., Mølbak, T., & Petersen, S. (2020). Building energy flexibility as an asset in system-wide district heating optimization models—Research—Aarhus University. https://ewds.strath.ac.uk/etp/Home.aspx
  • Kristensen and Petersen—2020—District heating energy efficiency of Danish build.pdf. (n.d.).
  • Kristensen, M. H., & Petersen, S. (2020). District heating energy efficiency of Danish building typologies. Energy and Buildings, 110602. https://doi.org/10.1016/j.enbuild.2020.110602
  • Kukkonen, V., Kücükavci, A., Seidenschnur, M., Rasmussen, M. H., Smith, K. M., & Hviid, C. A. (2022). An ontology to support flow system descriptions from design to operation of buildings. Automation in Construction, 134, 104067. https://doi.org/10.1016/j.autcon.2021.104067
  • Liu, X., Lai, Z., Wang, X., Huang, L., & Nielsen, P. S. (2020). A Contextual Anomaly Detection Framework for Energy Smart Meter Data Stream. In H. Yang, K. Pasupa, A. C.-S. Leung, J. T. Kwok, J. H. Chan, & I. King (Eds.), Neural Information Processing (pp. 733–742). Springer International Publishing. https://doi.org/10.1007/978-3-030-63823-8_83
  • Liu, X., Niu, Z., Yang, Y., Wu, J., Cheng, D., & Wang, X. (2020). VAP: a visual analysis tool for energy consumption spatio-temporal pattern discovery. In International Conference on Extending Database Technology. Proceedings of the 23rd International Conference on Extending Database Technology. https://doi.org/10.5441/002/edbt.2020.68
  • Mehmood, H., Gilman, E., Cortes, M., Kostakos, P., Byrne, K., Valta, S., Tekes, J., & Riekki, J. (2019). Implementing big data lake for heterogeneous data sources. 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW).
  • Møller Sneum, D., Rosenlund Soysal, E., Nesje, F., & Freeman, M. (2022, April 21). Embedded Discounting and the Green Transition. https://doi.org/10.2139/ssrn.4089463
  • Nielsen, K., Johansen, C., Johnsen, A., Sneum, D., & Nielsen, P. S. (2021). Automating analyses and exploring investment optimisation in energyPRO. DTU Management Engineering.
  • Nielsen, V. (n.d.). The business case of using digital twin for failure detection in radiator heating systems. (Draft – to Be Decided).
  • Niu, Z., Wu, J., Liu, X., Huang, L., & Nielsen, P. S. (2021). Understanding energy demand behaviors through spatio-temporal smart meter data analysis. Energy, 226, 120493. https://doi.org/10.1016/j.energy.2021.120493
  • Østergaard, D. S., Smith, K. M., Tunzi, M., & Svendsen, S. (2022). Low-temperature operation of heating systems to enable 4th generation district heating: A review. Energy, 248, 123529. https://doi.org/10.1016/j.energy.2022.123529
  • Østergaard, P. A., Andersen, A. N., & Sorknæs, P. (2022). The business-economic energy system modelling tool energyPRO. Energy, 257, 124792. https://doi.org/10.1016/j.energy.2022.124792
  • Oussous, A., Benjelloun, F.-Z., Ait Lahcen, A., & Belfkih, S. (2018). Big Data technologies: A survey. Journal of King Saud University – Computer and Information Sciences, 30(4), 431–448. https://doi.org/10.1016/j.jksuci.2017.06.001
  • Peng, J., Kimmig, A., Niu, Z., Wang, J., Liu, X., & Ovtcharova, J. (2021). A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework. Applied Energy, 299, 117321. https://doi.org/10.1016/j.apenergy.2021.117321
  • Petrovic, S., Bühler, F., & Bosack, M. (2019). Industrial excess heat in ambitious emission reduction scenarios for Denmark. Poster presented [Conference presentation]. EMP-E conference, Brussels, Belgium.
  • Qu, W., Liu, X., & Dessloch, S. (2021). A Workload-Aware Change Data Capture Framework for Data Warehousing. In M. Golfarelli, R. Wrembel, G. Kotsis, A. M. Tjoa, & I. Khalil (Eds.), Big Data Analytics and Knowledge Discovery (pp. 222–231). Springer International Publishing. https://doi.org/10.1007/978-3-030-86534-4_21
  • Ravn, H. (2022). Balmorel. An open source energy system model. http://www.balmorel.com/
  • Sarran, L., Smith, K. M., Hviid, C. A., & Rode, C. (2022). Grey-Box Modelling and Virtual Sensors Enabling Continuous Commissioning of Hydronic Floor Heating (SSRN Scholarly Paper ID 4027424). Social Science Research Network. https://doi.org/10.2139/ssrn.4027424
  • Schledorn, A., Guericke, D., Andersen, A., & Madsen, H. (2020). An Advanced Optimization-Based Bidding Method for District Heating Providers Considering Uncertainty and Block Bids [Conference presentation]. Smart Energy Conference, Aalborg, Denmark. https://backend.orbit.dtu.dk/ws/portalfiles/portal/255759634/0007.pdf
  • Schledorn, A., Guericke, D., Andersen, A. N., & Madsen, H. (2021). Optimising block bids of district heating operators to the day-ahead electricity market using stochastic programming. Smart Energy, 1, 100004. https://doi.org/10.1016/j.segy.2021.100004
  • Schledorn, A., Junker, R. G., Guericke, D., Madsen, H., & Dominković, D. F. (2022). Frigg: Soft-linking energy system and demand response models. Applied Energy, 317, 119074. https://doi.org/10.1016/j.apenergy.2022.119074
  • Schwarz, R., Lacalandra, F., Schewe, L., Bettinelli, A., Vigo, D., Bischi, A., Parriani, T., Martelli, E., Vuik, K., Lenz, R., Madsen, H., Blanco, I., Guericke, D., Yüksel-Ergün, I., & Zittel, J. (2021). Network and Storage. In N. S. Hadjidimitriou, A. Frangioni, T. Koch, & A. Lodi (Eds.), Mathematical Optimization for Efficient and Robust Energy Networks (Vol. 4, pp. 89–105). Springer International Publishing. https://doi.org/10.1007/978-3-030-57442-0_6
  • Siddique Bilal Muhammad, G. J. I., Nielsen Per Sieverts, Berg Rosendal Mathias, Keles Dogan. (2022). Decarbanisation pathways for District heating; a danish case study. Applied Energy.
  • Sieverts Nielsen, P., Siddique Khan, B., & Møller Sneum, D. (2022). Excess Heat Regulations, Contracts and Business Models – With a Special Focus on Excess Heat Opportunities in Denmark. 2022 6th International Conference on Green Energy and Applications (ICGEA), 68–81. https://doi.org/10.1109/ICGEA54406.2022.9792066
  • Sneum, D. (2021). Discounting in district energy. Smart Energy Systems Conference, Copenhagen, Denmark. https://smartenergysystems.eu/2021-2/
  • Sneum, M., Daniel, Soysal, R., Emilie, Nesje, F., & Freeman, C. M. (2021). Discounting assumptions in district energy [Conference presentation]. 7th International Conference on Smart Energy Systems, Copenhagen, Denmark.
  • Tahiri, A., Smith, K. M., Thorsen, J. E., Hviid, C. A., & Svendsen, S. (2022). Staged control of domestic hot water storage tanks to support district heating efficiency.
  • The Danish National Energy Data Lake: Requirements, Technical Architecture, and Tool Selection | IEEE Conference Publication | IEEE Xplore. (n.d.). Retrieved 10 February 2022, from https://ieeexplore.ieee.org/abstract/document/9378368
  • Thilker, C. A., Bacher, P., Bergsteinsson, H. G., Junker, R. G., Cali, D., & Madsen, H. (2021). Non-linear grey-box modelling for heat dynamics of buildings. Energy and Buildings, 252, 111457. https://doi.org/10.1016/j.enbuild.2021.111457
  • Thilker, C. A., Bacher, P., Cali, D., & Madsen, H. (2022). Identification of non-linear autoregressive models with exogenous inputs for room air temperature modelling. Energy and AI, 9, 100165. https://doi.org/10.1016/j.egyai.2022.100165
  • Thilker, C. A., Bergsteinsson, H. G., Bacher, P., Madsen, H., Calì, D., & Junker, R. G. (2021). Non-linear Model Predictive Control for Smart Heating of Buildings. E3S Web of Conferences, 246, 09005. https://doi.org/10.1051/e3sconf/202124609005
  • Thilker, C. A., Junker, R. G., Bacher, P., Jørgensen, J. B., & Madsen, H. (2021). Model Predictive Control Based on Stochastic Grey-Box Models. In S. Ploix, M. Amayri, & N. Bouguila (Eds.), Towards Energy Smart Homes (pp. 329–380). Springer International Publishing. https://doi.org/10.1007/978-3-030-76477-7_11
  • Thilker, C. A., Madsen, H., & Jørgensen, J. B. (2021). Advanced forecasting and disturbance modelling for model predictive control of smart energy systems. Applied Energy, 292, 116889. https://doi.org/10.1016/j.apenergy.2021.116889
  • Thorsen, J. E. (2021). Insights on domestic hot water consumption for multi flat buildings. Smart Energy System, Copenhagen.
  • Thorsen, J. E. (2022). Adaptive control strategy for domestic hot water storage tank supplied by district heating.
  • Thorsen, J. E., Busk, S., Frederik, Gynyel, F., & Wahlroos, M. (2021). Insights on Domestic Hot Water Consumption for Multi Flat Buildings. 7th international Conference on Smart Energy Systems.
  • Wu, J., Niu, Z., Huang, L., Nielsen, P. S., & Liu, X. (n.d.). Understanding Multi-Scale Spatio-Temporal Energy Consumption Data: A Visual Analysis Approach. Energy.
  • Bergsteinsson, H. G., Nielsen, T. S., Møller, J. K., Amer, S. B., Dominković, D. F., & Madsen, H. (2021). Use of smart meters as feedback for district heating temperature control. Energy Reports, 7, 213–221. https://doi.org/10.1016/j.egyr.2021.08.153
  • Liu, X., Niu, Z., Yang, Y., Wu, J., Cheng, D., & Wang, X. (2020). VAP: a visual analysis tool for energy consumption spatio-temporal pattern discovery. In International Conference on Extending Database Technology. Proceedings of the 23rd International Conference on Extending Database Technology. https://doi.org/10.5441/002/edbt.2020.68