IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v252y2019ic13.html
   My bibliography  Save this article

A data-driven approach for discovering heat load patterns in district heating

Author

Listed:
  • Calikus, Ece
  • Nowaczyk, Sławomir
  • Sant'Anna, Anita
  • Gadd, Henrik
  • Werner, Sven

Abstract

Understanding the heat usage of customers is crucial for effective district heating operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce. Most previous studies are limited to small-scale analyses that are not representative enough to understand the behavior of the overall network. In this work, we propose a data-driven approach that enables large-scale automatic analysis of heat load patterns in district heating networks without requiring prior knowledge. Our method clusters the customer profiles into different groups, extracts their representative patterns, and detects unusual customers whose profiles deviate significantly from the rest of their group. Using our approach, we present the first large-scale, comprehensive analysis of the heat load patterns by conducting a case study on many buildings in six different customer categories connected to two district heating networks in the south of Sweden. The 1222 buildings had a total floor space of 3.4 million square meters and used 1540 TJ heat during 2016. The results show that the proposed method has a high potential to be deployed and used in practice to analyze and understand customers’ heat-use habits.

Suggested Citation

  • Calikus, Ece & Nowaczyk, Sławomir & Sant'Anna, Anita & Gadd, Henrik & Werner, Sven, 2019. "A data-driven approach for discovering heat load patterns in district heating," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:252:y:2019:i:c:13
    DOI: 10.1016/j.apenergy.2019.113409
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919310839
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.113409?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Münster, Marie & Morthorst, Poul Erik & Larsen, Helge V. & Bregnbæk, Lars & Werling, Jesper & Lindboe, Hans Henrik & Ravn, Hans, 2012. "The role of district heating in the future Danish energy system," Energy, Elsevier, vol. 48(1), pages 47-55.
    2. Christoph Flath & David Nicolay & Tobias Conte & Clemens Dinther & Lilia Filipova-Neumann, 2012. "Cluster Analysis of Smart Metering Data," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 4(1), pages 31-39, February.
    3. Lund, H. & Möller, B. & Mathiesen, B.V. & Dyrelund, A., 2010. "The role of district heating in future renewable energy systems," Energy, Elsevier, vol. 35(3), pages 1381-1390.
    4. Lund, Henrik & Østergaard, Poul Alberg & Chang, Miguel & Werner, Sven & Svendsen, Svend & Sorknæs, Peter & Thorsen, Jan Eric & Hvelplund, Frede & Mortensen, Bent Ole Gram & Mathiesen, Brian Vad & Boje, 2018. "The status of 4th generation district heating: Research and results," Energy, Elsevier, vol. 164(C), pages 147-159.
    5. Gadd, Henrik & Werner, Sven, 2014. "Achieving low return temperatures from district heating substations," Applied Energy, Elsevier, vol. 136(C), pages 59-67.
    6. Ma, Zhenjun & Yan, Rui & Nord, Natasa, 2017. "A variation focused cluster analysis strategy to identify typical daily heating load profiles of higher education buildings," Energy, Elsevier, vol. 134(C), pages 90-102.
    7. Goia, Aldo & May, Caterina & Fusai, Gianluca, 2010. "Functional clustering and linear regression for peak load forecasting," International Journal of Forecasting, Elsevier, vol. 26(4), pages 700-711, October.
    8. Connolly, D. & Lund, H. & Mathiesen, B.V. & Werner, S. & Möller, B. & Persson, U. & Boermans, T. & Trier, D. & Østergaard, P.A. & Nielsen, S., 2014. "Heat Roadmap Europe: Combining district heating with heat savings to decarbonise the EU energy system," Energy Policy, Elsevier, vol. 65(C), pages 475-489.
    9. Rezaie, Behnaz & Rosen, Marc A., 2012. "District heating and cooling: Review of technology and potential enhancements," Applied Energy, Elsevier, vol. 93(C), pages 2-10.
    10. Fang, Hao & Xia, Jianjun & Zhu, Kan & Su, Yingbo & Jiang, Yi, 2013. "Industrial waste heat utilization for low temperature district heating," Energy Policy, Elsevier, vol. 62(C), pages 236-246.
    11. Noussan, Michel & Jarre, Matteo & Poggio, Alberto, 2017. "Real operation data analysis on district heating load patterns," Energy, Elsevier, vol. 129(C), pages 70-78.
    12. Félix Iglesias & Wolfgang Kastner, 2013. "Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns," Energies, MDPI, vol. 6(2), pages 1-19, January.
    13. Gadd, Henrik & Werner, Sven, 2013. "Heat load patterns in district heating substations," Applied Energy, Elsevier, vol. 108(C), pages 176-183.
    14. Gadd, Henrik & Werner, Sven, 2015. "Fault detection in district heating substations," Applied Energy, Elsevier, vol. 157(C), pages 51-59.
    15. Lund, Henrik & Werner, Sven & Wiltshire, Robin & Svendsen, Svend & Thorsen, Jan Eric & Hvelplund, Frede & Mathiesen, Brian Vad, 2014. "4th Generation District Heating (4GDH)," Energy, Elsevier, vol. 68(C), pages 1-11.
    16. Verda, Vittorio & Colella, Francesco, 2011. "Primary energy savings through thermal storage in district heating networks," Energy, Elsevier, vol. 36(7), pages 4278-4286.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anders Rhiger Hansen & Daniel Leiria & Hicham Johra & Anna Marszal-Pomianowska, 2022. "Who Produces the Peaks? Household Variation in Peak Energy Demand for Space Heating and Domestic Hot Water," Energies, MDPI, vol. 15(24), pages 1-23, December.
    2. Zhang, Fan & Bales, Chris & Fleyeh, Hasan, 2021. "Night setback identification of district heat substations using bidirectional long short term memory with attention mechanism," Energy, Elsevier, vol. 224(C).
    3. Liu, Guoqiang & Zhou, Xuan & Yan, Junwei & Yan, Gang, 2021. "A temperature and time-sharing dynamic control approach for space heating of buildings in district heating system," Energy, Elsevier, vol. 221(C).
    4. Abolfazl Rezaei & Bahador Samadzadegan & Hadise Rasoulian & Saeed Ranjbar & Soroush Samareh Abolhassani & Azin Sanei & Ursula Eicker, 2021. "A New Modeling Approach for Low-Carbon District Energy System Planning," Energies, MDPI, vol. 14(5), pages 1-22, March.
    5. Du, Han & Zhou, Xinlei & Nord, Natasa & Carden, Yale & Ma, Zhenjun, 2023. "A new data mining strategy for performance evaluation of a shared energy recovery system integrated with data centres and district heating networks," Energy, Elsevier, vol. 285(C).
    6. Neumayer, Martin & Stecher, Dominik & Grimm, Sebastian & Maier, Andreas & Bücker, Dominikus & Schmidt, Jochen, 2023. "Fault and anomaly detection in district heating substations: A survey on methodology and data sets," Energy, Elsevier, vol. 276(C).
    7. Rémi Delage & Toshihiko Nakata, 2022. "Multivariate Empirical Mode Decomposition and Recurrence Quantification for the Multiscale, Spatiotemporal Analysis of Electricity Demand—A Case Study of Japan," Energies, MDPI, vol. 15(17), pages 1-17, August.
    8. Zhong, Wei & Huang, Wei & Lin, Xiaojie & Li, Zhongbo & Zhou, Yi, 2020. "Research on data-driven identification and prediction of heat response time of urban centralized heating system," Energy, Elsevier, vol. 212(C).
    9. Salman Siddiqui & Mark Barrett & John Macadam, 2021. "A High Resolution Spatiotemporal Urban Heat Load Model for GB," Energies, MDPI, vol. 14(14), pages 1-28, July.
    10. Yuan, Jianjuan & Zhou, Zhihua & Tang, Huajie & Wang, Chendong & Lu, Shilei & Han, Zhao & Zhang, Ji & Sheng, Ying, 2020. "Identification heat user behavior for improving the accuracy of heating load prediction model based on wireless on-off control system," Energy, Elsevier, vol. 199(C).
    11. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    12. Schaffer, Markus & Vera-Valdés, J. Eduardo & Marszal-Pomianowska, Anna, 2024. "Exploring smart heat meter data: A co-clustering driven approach to analyse the energy use of single-family houses," Applied Energy, Elsevier, vol. 371(C).
    13. Fouladvand, Javanshir & Aranguren Rojas, Maria & Hoppe, Thomas & Ghorbani, Amineh, 2022. "Simulating thermal energy community formation: Institutional enablers outplaying technological choice," Applied Energy, Elsevier, vol. 306(PA).
    14. Triebs, Merlin Sebastian & Tsatsaronis, George, 2022. "From heat demand to heat supply: How to obtain more accurate feed-in time series for district heating systems," Applied Energy, Elsevier, vol. 311(C).
    15. Shahrooz Abghari & Veselka Boeva & Jens Brage & Håkan Grahn, 2020. "A Higher Order Mining Approach for the Analysis of Real-World Datasets," Energies, MDPI, vol. 13(21), pages 1-23, November.
    16. Vahid Arabzadeh & Peter D. Lund, 2020. "Effect of Heat Demand on Integration of Urban Large-Scale Renewable Schemes—Case of Helsinki City (60 °N)," Energies, MDPI, vol. 13(9), pages 1-17, May.
    17. Lankeshwara, Gayan & Sharma, Rahul & Yan, Ruifeng & Saha, Tapan K., 2022. "Control algorithms to mitigate the effect of uncertainties in residential demand management," Applied Energy, Elsevier, vol. 306(PA).
    18. Mengyao Lu & Guitao Xu & Jianjuan Yuan, 2023. "Installation Principle and Calculation Model of the Representative Indoor Temperature-Monitoring Points in Large-Scale Buildings," Energies, MDPI, vol. 16(17), pages 1-19, September.
    19. Huang, Yaohui & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng & Fu, Yonggang, 2024. "Sparse dynamic graph learning for district heat load forecasting," Applied Energy, Elsevier, vol. 371(C).
    20. Sara Månsson & Marcus Thern & Per-Olof Johansson Kallioniemi & Kerstin Sernhed, 2021. "A Fault Handling Process for Faults in District Heating Customer Installations," Energies, MDPI, vol. 14(11), pages 1-18, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Danica Djurić Ilić, 2020. "Classification of Measures for Dealing with District Heating Load Variations—A Systematic Review," Energies, MDPI, vol. 14(1), pages 1-27, December.
    2. Guelpa, Elisa & Verda, Vittorio, 2020. "Automatic fouling detection in district heating substations: Methodology and tests," Applied Energy, Elsevier, vol. 258(C).
    3. Fester, Jakob & Østergaard, Peter Friis & Bentsen, Fredrik & Nielsen, Brian Kongsgaard, 2023. "A data-driven method for heat loss estimation from district heating service pipes using heat meter- and GIS data," Energy, Elsevier, vol. 277(C).
    4. Li, Haoran & Hou, Juan & Hong, Tianzhen & Ding, Yuemin & Nord, Natasa, 2021. "Energy, economic, and environmental analysis of integration of thermal energy storage into district heating systems using waste heat from data centres," Energy, Elsevier, vol. 219(C).
    5. Yuan, Jianjuan & Zhou, Zhihua & Tang, Huajie & Wang, Chendong & Lu, Shilei & Han, Zhao & Zhang, Ji & Sheng, Ying, 2020. "Identification heat user behavior for improving the accuracy of heating load prediction model based on wireless on-off control system," Energy, Elsevier, vol. 199(C).
    6. Yang, Libing & Entchev, Evgueniy & Rosato, Antonio & Sibilio, Sergio, 2017. "Smart thermal grid with integration of distributed and centralized solar energy systems," Energy, Elsevier, vol. 122(C), pages 471-481.
    7. Zhang, Fan & Bales, Chris & Fleyeh, Hasan, 2021. "Night setback identification of district heat substations using bidirectional long short term memory with attention mechanism," Energy, Elsevier, vol. 224(C).
    8. Hansen, Kenneth & Connolly, David & Lund, Henrik & Drysdale, David & Thellufsen, Jakob Zinck, 2016. "Heat Roadmap Europe: Identifying the balance between saving heat and supplying heat," Energy, Elsevier, vol. 115(P3), pages 1663-1671.
    9. Xiaofeng Guo & Alain Pascal Goumba & Cheng Wang, 2019. "Comparison of Direct and Indirect Active Thermal Energy Storage Strategies for Large-Scale Solar Heating Systems," Energies, MDPI, vol. 12(10), pages 1-18, May.
    10. Jie, Pengfei & Kong, Xiangfei & Rong, Xian & Xie, Shangqun, 2016. "Selecting the optimum pressure drop per unit length of district heating piping network based on operating strategies," Applied Energy, Elsevier, vol. 177(C), pages 341-353.
    11. Østergaard, Dorte Skaarup & Smith, Kevin Michael & Tunzi, Michele & Svendsen, Svend, 2022. "Low-temperature operation of heating systems to enable 4th generation district heating: A review," Energy, Elsevier, vol. 248(C).
    12. Li, Yu & Rezgui, Yacine & Zhu, Hanxing, 2017. "District heating and cooling optimization and enhancement – Towards integration of renewables, storage and smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 281-294.
    13. Averfalk, Helge & Werner, Sven, 2018. "Novel low temperature heat distribution technology," Energy, Elsevier, vol. 145(C), pages 526-539.
    14. Persson, U. & Möller, B. & Werner, S., 2014. "Heat Roadmap Europe: Identifying strategic heat synergy regions," Energy Policy, Elsevier, vol. 74(C), pages 663-681.
    15. Serafeim Moustakidis & Ioannis Meintanis & George Halikias & Nicos Karcanias, 2019. "An Innovative Control Framework for District Heating Systems: Conceptualisation and Preliminary Results," Resources, MDPI, vol. 8(1), pages 1-15, January.
    16. Østergaard, Dorte Skaarup & Tunzi, Michele & Svendsen, Svend, 2021. "What does a well-functioning heating system look like? Investigation of ten Danish buildings that utilize district heating efficiently," Energy, Elsevier, vol. 227(C).
    17. Averfalk, Helge & Werner, Sven, 2020. "Economic benefits of fourth generation district heating," Energy, Elsevier, vol. 193(C).
    18. Sarran, Lucile & Smith, Kevin M. & Hviid, Christian A. & Rode, Carsten, 2022. "Grey-box modelling and virtual sensors enabling continuous commissioning of hydronic floor heating," Energy, Elsevier, vol. 261(PB).
    19. Hong, Yejin & Yoon, Sungmin, 2022. "Holistic Operational Signatures for an energy-efficient district heating substation in buildings," Energy, Elsevier, vol. 250(C).
    20. Welsch, Bastian & Göllner-Völker, Laura & Schulte, Daniel O. & Bär, Kristian & Sass, Ingo & Schebek, Liselotte, 2018. "Environmental and economic assessment of borehole thermal energy storage in district heating systems," Applied Energy, Elsevier, vol. 216(C), pages 73-90.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:252:y:2019:i:c:13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.