IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v217y2021ics0360544220324543.html
   My bibliography  Save this article

Heat demand forecasting algorithm for a Warsaw district heating network

Author

Listed:
  • Kurek, Teresa
  • Bielecki, Artur
  • Świrski, Konrad
  • Wojdan, Konrad
  • Guzek, Michał
  • Białek, Jakub
  • Brzozowski, Rafał
  • Serafin, Rafał

Abstract

This paper presents a complex analysis of heat demand forecasting methods for the Warsaw District Heating Network, which is owned by Veolia Energia Warszawa, the largest district heating network (DHN) in the European Union. The analyzed network supplies heat for both domestic and heating purposes. Therefore, summer, intermediate, and winter seasons were delineated and separately evaluated. Numerous models were utilized including models broadly recognized and used (ridge regression, autoregression with exogenous input, deep artificial neural networks), as well as previously unexplored models (combination of summer and winter linear models with the utilization of fuzzy logic). A 72 h forecast horizon is evaluated for total heat demand (the sum of all substations), as well as for groups of buildings (local models for specific city areas), and individually for the majority of substations. Models of areas use an additional input variable, namely, the results of the total heat demand forecast, and are proposed to be developed as an auxiliary information variable offered to grid operators. An artificial neural network based model achieves the best accuracy for all analyzed seasons. The intermediate seasons prove to be the most difficult to accurately forecast for and only the combination of summer and winter linear autoregresive models with utilization of a fuzzy logic reached comparable accuracy.

Suggested Citation

  • Kurek, Teresa & Bielecki, Artur & Świrski, Konrad & Wojdan, Konrad & Guzek, Michał & Białek, Jakub & Brzozowski, Rafał & Serafin, Rafał, 2021. "Heat demand forecasting algorithm for a Warsaw district heating network," Energy, Elsevier, vol. 217(C).
    • Handle: RePEc:eee:energy:v:217:y:2021:i:c:s0360544220324543
    DOI: 10.1016/j.energy.2020.119347
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220324543
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.119347?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. 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.
    2. Magnus Dahl & Adam Brun & Oliver S. Kirsebom & Gorm B. Andresen, 2018. "Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data," Energies, MDPI, vol. 11(7), pages 1-16, June.
    3. Suryanarayana, Gowri & Lago, Jesus & Geysen, Davy & Aleksiejuk, Piotr & Johansson, Christian, 2018. "Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods," Energy, Elsevier, vol. 157(C), pages 141-149.
    4. Dahl, Magnus & Brun, Adam & Andresen, Gorm B., 2017. "Using ensemble weather predictions in district heating operation and load forecasting," Applied Energy, Elsevier, vol. 193(C), pages 455-465.
    5. Ahmad, Tanveer & Zhang, Hongcai, 2020. "Novel deep supervised ML models with feature selection approach for large-scale utilities and buildings short and medium-term load requirement forecasts," Energy, Elsevier, vol. 209(C).
    6. Volkova, Anna & Mašatin, Vladislav & Siirde, Andres, 2018. "Methodology for evaluating the transition process dynamics towards 4th generation district heating networks," Energy, Elsevier, vol. 150(C), pages 253-261.
    7. Fang, Tingting & Lahdelma, Risto, 2016. "Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system," Applied Energy, Elsevier, vol. 179(C), pages 544-552.
    8. Kristensen, Martin Heine & Hedegaard, Rasmus Elbæk & Petersen, Steffen, 2020. "Long-term forecasting of hourly district heating loads in urban areas using hierarchical archetype modeling," Energy, Elsevier, vol. 201(C).
    9. 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.
    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. Saloux, Etienne & Runge, Jason & Zhang, Kun, 2023. "Operation optimization of multi-boiler district heating systems using artificial intelligence-based model predictive control: Field demonstrations," Energy, Elsevier, vol. 285(C).
    2. Gong, Mingju & Zhao, Yin & Sun, Jiawang & Han, Cuitian & Sun, Guannan & Yan, Bo, 2022. "Load forecasting of district heating system based on Informer," Energy, Elsevier, vol. 253(C).
    3. Kılkış, Şiir, 2021. "Transition towards urban system integration and benchmarking of an urban area to accelerate mitigation towards net-zero targets," Energy, Elsevier, vol. 236(C).
    4. Szabolcs Kováč & German Micha’čonok & Igor Halenár & Pavel Važan, 2021. "Comparison of Heat Demand Prediction Using Wavelet Analysis and Neural Network for a District Heating Network," Energies, MDPI, vol. 14(6), pages 1-20, March.
    5. Frafjord, Aksel Johan & Radicke, Jan-Philip & Keprate, Arvind & Komulainen, Tiina M., 2024. "Data-driven approaches for deriving a soft sensor in a district heating network," Energy, Elsevier, vol. 292(C).
    6. Seul-Ye Lim & Jeoung-Sik Min & Seung-Hoon Yoo, 2021. "Price and Income Elasticities of Residential Heat Demand from District Heating System: A Price Sensitivity Measurement Experiment in South Korea," Sustainability, MDPI, vol. 13(13), pages 1-10, June.
    7. Chung, Won Hee & Gu, Yeong Hyeon & Yoo, Seong Joon, 2022. "District heater load forecasting based on machine learning and parallel CNN-LSTM attention," Energy, Elsevier, vol. 246(C).
    8. Chicherin, Stanislav & Starikov, Aleksander & Zhuikov, Andrey, 2022. "Justifying network reconstruction when switching to low temperature district heating," Energy, Elsevier, vol. 248(C).
    9. Runge, Jason & Saloux, Etienne, 2023. "A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system," Energy, Elsevier, vol. 269(C).
    10. Chen, Minghao & Xie, Zhiyuan & Sun, Yi & Zheng, Shunlin, 2023. "The predictive management in campus heating system based on deep reinforcement learning and probabilistic heat demands forecasting," Applied Energy, Elsevier, vol. 350(C).
    11. Manservigi, Lucrezia & Bahlawan, Hilal & Losi, Enzo & Morini, Mirko & Spina, Pier Ruggero & Venturini, Mauro, 2022. "A diagnostic approach for fault detection and identification in district heating networks," Energy, Elsevier, vol. 251(C).
    12. Pavel Rušeljuk & Kertu Lepiksaar & Andres Siirde & Anna Volkova, 2021. "Economic Dispatch of CHP Units through District Heating Network’s Demand-Side Management," Energies, MDPI, vol. 14(15), pages 1-20, July.

    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. Mengting Jiang & Camilo Rindt & David M. J. Smeulders, 2022. "Optimal Planning of Future District Heating Systems—A Review," Energies, MDPI, vol. 15(19), pages 1-38, September.
    2. Chicherin, Stanislav & Anvari-Moghaddam, Amjad, 2021. "Adjusting heat demands using the operational data of district heating systems," Energy, Elsevier, vol. 235(C).
    3. Kristensen, Martin Heine & Hedegaard, Rasmus Elbæk & Petersen, Steffen, 2020. "Long-term forecasting of hourly district heating loads in urban areas using hierarchical archetype modeling," Energy, Elsevier, vol. 201(C).
    4. Costanza Saletti & Mirko Morini & Agostino Gambarotta, 2020. "The Status of Research and Innovation on Heating and Cooling Networks as Smart Energy Systems within Horizon 2020," Energies, MDPI, vol. 13(11), pages 1-27, June.
    5. Yuan, Jianjuan & Huang, Ke & Han, Zhao & Wang, Chendong & Lu, Shilei & Zhou, Zhihua, 2022. "Evaluation of the operation data for improving the prediction accuracy of heating parameters in heating substation," Energy, Elsevier, vol. 238(PB).
    6. Nielsen, Tore Bach & Lund, Henrik & Østergaard, Poul Alberg & Duic, Neven & Mathiesen, Brian Vad, 2021. "Perspectives on energy efficiency and smart energy systems from the 5th SESAAU2019 conference," Energy, Elsevier, vol. 216(C).
    7. Xue, Puning & Jiang, Yi & Zhou, Zhigang & Chen, Xin & Fang, Xiumu & Liu, Jing, 2019. "Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms," Energy, Elsevier, vol. 188(C).
    8. Gong, Mingju & Zhao, Yin & Sun, Jiawang & Han, Cuitian & Sun, Guannan & Yan, Bo, 2022. "Load forecasting of district heating system based on Informer," Energy, Elsevier, vol. 253(C).
    9. Chicherin, Stanislav, 2020. "Methodology for analyzing operation data for optimum district heating (DH) system design: Ten-year data of Omsk, Russia," Energy, Elsevier, vol. 211(C).
    10. Stanislav Chicherin & Vladislav Mašatin & Andres Siirde & Anna Volkova, 2020. "Method for Assessing Heat Loss in A District Heating Network with A Focus on the State of Insulation and Actual Demand for Useful Energy," Energies, MDPI, vol. 13(17), pages 1-15, September.
    11. Maciej Bujalski & Paweł Madejski, 2021. "Forecasting of Heat Production in Combined Heat and Power Plants Using Generalized Additive Models," Energies, MDPI, vol. 14(8), pages 1-15, April.
    12. Pieper, Henrik & Krupenski, Igor & Brix Markussen, Wiebke & Ommen, Torben & Siirde, Andres & Volkova, Anna, 2021. "Method of linear approximation of COP for heat pumps and chillers based on thermodynamic modelling and off-design operation," Energy, Elsevier, vol. 230(C).
    13. Lund, Henrik & Duic, Neven & Østergaard, Poul Alberg & Mathiesen, Brian Vad, 2018. "Future district heating systems and technologies: On the role of smart energy systems and 4th generation district heating," Energy, Elsevier, vol. 165(PA), pages 614-619.
    14. Meibodi, Saleh S. & Loveridge, Fleur, 2022. "The future role of energy geostructures in fifth generation district heating and cooling networks," Energy, Elsevier, vol. 240(C).
    15. Zhao, Yin & Gong, Mingju & Sun, Jiawang & Han, Cuitian & Jing, Lei & Li, Bo & Zhao, Zhixuan, 2023. "A new hybrid optimization prediction strategy based on SH-Informer for district heating system," Energy, Elsevier, vol. 282(C).
    16. Van Oevelen, Tijs & Vanhoudt, Dirk & Johansson, Christian & Smulders, Ed, 2020. "Testing and performance evaluation of the STORM controller in two demonstration sites," Energy, Elsevier, vol. 197(C).
    17. Lumbreras, Mikel & Garay-Martinez, Roberto & Arregi, Beñat & Martin-Escudero, Koldobika & Diarce, Gonzalo & Raud, Margus & Hagu, Indrek, 2022. "Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters," Energy, Elsevier, vol. 239(PD).
    18. Yuan, Jianjuan & Huang, Ke & Lu, Shilei & Zhang, Ji & Han, Zhao & Zhou, Zhihua, 2022. "Analysis of influencing factors on heat consumption of large residential buildings with different occupancy rates-Tianjin case study," Energy, Elsevier, vol. 238(PC).
    19. Saletti, Costanza & Zimmerman, Nathan & Morini, Mirko & Kyprianidis, Konstantinos & Gambarotta, Agostino, 2021. "Enabling smart control by optimally managing the State of Charge of district heating networks," Applied Energy, Elsevier, vol. 283(C).
    20. Binglin Li & Yong Shao & Yufeng Lian & Pai Li & Qiang Lei, 2023. "Bayesian Optimization-Based LSTM for Short-Term Heating Load Forecasting," Energies, MDPI, vol. 16(17), pages 1-14, August.

    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:energy:v:217:y:2021:i:c:s0360544220324543. 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.journals.elsevier.com/energy .

    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.