IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v288y2024ics0360544223031201.html
   My bibliography  Save this article

Digital twin modeling for district heating network based on hydraulic resistance identification and heat load prediction

Author

Listed:
  • Zheng, Xuejing
  • Shi, Zhiyuan
  • Wang, Yaran
  • Zhang, Huan
  • Tang, Zhiyun

Abstract

As a vital infrastructure in modern cities, district heating (DH) systems provide stable high-quality heat sources. To satisfy the demands of energy-saving, emission-reduction policies, and user preferences, a high-precision digital simulation platform is essential for district heating network (DHN) scheduling. In this paper, a method for digital twin (DT) modeling based on hydraulic resistance identification using an online adaptive particle swarm optimization (APSO) algorithm is proposed. Additionally, Singular Spectrum Analysis and back propagation artificial neural network algorithms (SSA-BP) are proposed to achieve heat load prediction in order to provide sufficient conditions for DHN scheduling. The load prediction for the substation is completed by predicting the secondary return water temperature at the subsequent time step based on the historical outdoor temperature time series and secondary supply water temperature of the substation. Accordingly, simulation analysis has been conducted on its application to an existing DHN in Tianjin. With the proposed methods, the simulation errors are reduced for about 80 % substations in the case DHN compared to mechanistic models when simulating hydraulic conditions. Among these, 77.49 % of substations were reduced by 0 %–3 %, and 1.43 % of substations were reduced by 3 %–5 %. The prediction errors of secondary return water temperature for case substations A and B exhibit random variation trends, with maximum absolute errors within the range of 0.02 °C and 0.06 °C, respectively.

Suggested Citation

  • Zheng, Xuejing & Shi, Zhiyuan & Wang, Yaran & Zhang, Huan & Tang, Zhiyun, 2024. "Digital twin modeling for district heating network based on hydraulic resistance identification and heat load prediction," Energy, Elsevier, vol. 288(C).
    • Handle: RePEc:eee:energy:v:288:y:2024:i:c:s0360544223031201
    DOI: 10.1016/j.energy.2023.129726
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223031201
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129726?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. Wang, Yaran & Shi, Kaiyu & Zheng, Xuejing & You, Shijun & Zhang, Huan & Zhu, Chengzhi & Li, Liang & Wei, Shen & Ding, Chao & Wang, Na, 2020. "Thermo-hydraulic coupled analysis of meshed district heating networks based on improved breadth first search method," Energy, Elsevier, vol. 205(C).
    2. Sarwar, Riasat & Cho, Heejin & Cox, Sam J. & Mago, Pedro J. & Luck, Rogelio, 2017. "Field validation study of a time and temperature indexed autoregressive with exogenous (ARX) model for building thermal load prediction," Energy, Elsevier, vol. 119(C), pages 483-496.
    3. Zheng, Xuejing & Shi, Zhiyuan & Wang, Yaran & Zhang, Huan & Liu, Huzhen, 2023. "Thermo-hydraulic condition optimization of large-scale complex district heating network: A case study of Tianjin," Energy, Elsevier, vol. 266(C).
    4. Yongxin Liu & Peng Wang & Peng Luo, 2020. "Pipe Hydraulic Resistances Identification of District Heating Networks Based on Matrix Analysis," Energies, MDPI, vol. 13(11), pages 1-11, June.
    5. Zheng, Xuejing & Sun, Qihang & Wang, Yaran & Zheng, Lijun & Gao, Xinyong & You, Shijun & Zhang, Huan & Shi, Kaiyu, 2021. "Thermo-hydraulic coupled simulation and analysis of a real large-scale complex district heating network in Tianjin," Energy, Elsevier, vol. 236(C).
    6. 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.
    7. Guelpa, Elisa & Toro, Claudia & Sciacovelli, Adriano & Melli, Roberto & Sciubba, Enrico & Verda, Vittorio, 2016. "Optimal operation of large district heating networks through fast fluid-dynamic simulation," Energy, Elsevier, vol. 102(C), pages 586-595.
    8. Wang, Yaran & You, Shijun & Zhang, Huan & Zheng, Xuejing & Zheng, Wandong & Miao, Qingwei & Lu, Gang, 2017. "Thermal transient prediction of district heating pipeline: Optimal selection of the time and spatial steps for fast and accurate calculation," Applied Energy, Elsevier, vol. 206(C), pages 900-910.
    9. Izadyar, Nima & Ghadamian, Hossein & Ong, Hwai Chyuan & moghadam, Zeinab & Tong, Chong Wen & Shamshirband, Shahaboddin, 2015. "Appraisal of the support vector machine to forecast residential heating demand for the District Heating System based on the monthly overall natural gas consumption," Energy, Elsevier, vol. 93(P2), pages 1558-1567.
    10. 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).
    11. Mehdi Dini & Massoud Tabesh, 2014. "A New Method for Simultaneous Calibration of Demand Pattern and Hazen-Williams Coefficients in Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 2021-2034, May.
    12. Gustavo Meirelles & Daniel Manzi & Bruno Brentan & Thaisa Goulart & Edevar Luvizotto, 2017. "Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(13), pages 4339-4351, October.
    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. Dong, Zhe & Cheng, Zhonghua & Zhu, Yunlong & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2024. "Passivity-based control of fluid flow networks with capacitance," Energy, Elsevier, vol. 299(C).

    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. Liu, Zhikai & Zhang, Huan & Wang, Yaran & Fan, Xianwang & You, Shijun & Jiang, Yan & Gao, Xinlei, 2023. "Optimization of hydraulic distribution using loop adjustment method in meshed district heating system with multiple heat sources," Energy, Elsevier, vol. 284(C).
    2. Dong, Zhe & Cheng, Zhonghua & Zhu, Yunlong & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2024. "Passivity-based control of fluid flow networks with capacitance," Energy, Elsevier, vol. 299(C).
    3. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    4. Zheng, Xuejing & Shi, Zhiyuan & Wang, Yaran & Zhang, Huan & Liu, Huzhen, 2023. "Thermo-hydraulic condition optimization of large-scale complex district heating network: A case study of Tianjin," Energy, Elsevier, vol. 266(C).
    5. Zheng, Xuejing & Sun, Qihang & Wang, Yaran & Zheng, Lijun & Gao, Xinyong & You, Shijun & Zhang, Huan & Shi, Kaiyu, 2021. "Thermo-hydraulic coupled simulation and analysis of a real large-scale complex district heating network in Tianjin," Energy, Elsevier, vol. 236(C).
    6. Sun, Chunhua & Yuan, Lingyu & Cao, Shanshan & Xia, Guoqiang & Liu, Yanan & Wu, Xiangdong, 2023. "Identifying supply-demand mismatches in district heating system based on association rule mining," Energy, Elsevier, vol. 280(C).
    7. Liu, Zhikai & Zhang, Huan & Wang, Yaran & Jiang, Yan & He, Zhihao & Zhou, Pengkun, 2023. "An adaptive double-Newton-iteration hydraulic calculation method for optimal operation of the meshed district heating network," Energy, Elsevier, vol. 272(C).
    8. Andrea Menapace & Simone Santopietro & Rudy Gargano & Maurizio Righetti, 2021. "Stochastic Generation of District Heat Load," Energies, MDPI, vol. 14(17), pages 1-17, August.
    9. 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.
    10. 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).
    11. Shipeng Chu & Tuqiao Zhang & Chengna Xu & Tingchao Yu & Yu Shao, 2021. "Dealing with Data Missing and Outlier to Calibrate Nodal Water Demands in Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2863-2878, July.
    12. Xie, Zichan & Wang, Haichao & Hua, Pengmin & Lahdelma, Risto, 2023. "Discrete event simulation for dynamic thermal modelling of district heating pipe," Energy, Elsevier, vol. 285(C).
    13. Shakeel, Asim & Chong, Daotong & Wang, Jinshi, 2023. "Load forecasting of district heating system based on improved FB-Prophet model," Energy, Elsevier, vol. 278(C).
    14. Xue, Guixiang & Qi, Chengying & Li, Han & Kong, Xiangfei & Song, Jiancai, 2020. "Heating load prediction based on attention long short term memory: A case study of Xingtai," Energy, Elsevier, vol. 203(C).
    15. Boghetti, Roberto & Kämpf, Jérôme H., 2024. "Verification of an open-source Python library for the simulation of district heating networks with complex topologies," Energy, Elsevier, vol. 290(C).
    16. Tian, Xingtao & Lin, Xiaojie & Zhong, Wei & Zhou, Yi & Cong, Feiyun, 2024. "Optimal dispatch of integrated electricity and heating systems considering the quality-quantity regulation of heating systems to promote renewable energy consumption," Energy, Elsevier, vol. 300(C).
    17. Liu, Zhikai & Zhang, Huan & Wang, Yaran & Song, Zixu & You, Shijun & Jiang, Yan & Wu, Zhangxiang, 2022. "A thermal-hydraulic coupled simulation approach for the temperature and flow rate control strategy evaluation of the multi-room radiator heating system," Energy, Elsevier, vol. 246(C).
    18. Kuntuarova, Saltanat & Licklederer, Thomas & Huynh, Thanh & Zinsmeister, Daniel & Hamacher, Thomas & Perić, Vedran, 2024. "Design and simulation of district heating networks: A review of modeling approaches and tools," Energy, Elsevier, vol. 305(C).
    19. Chia-Cheng Shiu & Chih-Chung Chung & Tzuping Chiang, 2024. "Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 323-341, January.
    20. Vivian, Jacopo & Quaggiotto, Davide & Zarrella, Angelo, 2020. "Increasing the energy flexibility of existing district heating networks through flow rate variations," Applied Energy, Elsevier, vol. 275(C).

    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:288:y:2024:i:c:s0360544223031201. 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.