IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v245y2022ics0360544222001177.html
   My bibliography  Save this article

Research on control strategy integrated with characteristics of user's energy-saving behavior of district heating system

Author

Listed:
  • Sun, Chunhua
  • Liu, Yanan
  • Gao, Xiaoyu
  • Wang, Jinda
  • Yang, Lan
  • Qi, Chengyong

Abstract

Traditional control strategies in district heating system (DHS) are generally based on meteorological parameters and experience, which rarely consider user's energy-saving behaviors. In new heating mode that integrates heating metering and temperature control, the application of on-off temperature control valves (on-off TCV) makes it possible for users to save energy. This paper proposes a control strategy of DHS that integrates characteristics of user's energy-saving behaviors and realizes combined control of feedforward and feedback. Firstly, we identify user's heating usage based on set indoor temperature and TCV's closing time ratio, and divide regulation periods; secondly, the TCV's closing time ratio of system is used to predict heat load of different regulation periods under different heating modes; finally, the deviation between target indoor temperature and actual value is used to dynamically correct heat load, and then supply curve is obtained and applied. The results show that prediction accuracy is improved. Meanwhile, user's indoor temperature is closer to target value and the system realized energy-saving operation. The energy-saving rates are 4.6%, 2.63% and 19.83% respectively in early cold, alpine cold and late cold periods. Throughout heating season, the proportion of TCV's closing time has a little change and the hydraulic condition is stable.

Suggested Citation

  • Sun, Chunhua & Liu, Yanan & Gao, Xiaoyu & Wang, Jinda & Yang, Lan & Qi, Chengyong, 2022. "Research on control strategy integrated with characteristics of user's energy-saving behavior of district heating system," Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:energy:v:245:y:2022:i:c:s0360544222001177
    DOI: 10.1016/j.energy.2022.123214
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222001177
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123214?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. Gu, Jihao & Wang, Jin & Qi, Chengying & Min, Chunhua & Sundén, Bengt, 2018. "Medium-term heat load prediction for an existing residential building based on a wireless on-off control system," Energy, Elsevier, vol. 152(C), pages 709-718.
    2. 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).
    3. Yuan, Jianjuan & Wang, Chendong & Zhou, Zhihua, 2019. "Study on refined control and prediction model of district heating station based on support vector machine," Energy, Elsevier, vol. 189(C).
    4. Nigitz, Thomas & Gölles, Markus, 2019. "A generally applicable, simple and adaptive forecasting method for the short-term heat load of consumers," Applied Energy, Elsevier, vol. 241(C), pages 73-81.
    5. 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.
    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. 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.
    8. Sun, Chunhua & Chen, Jiali & Cao, Shanshan & Gao, Xiaoyu & Xia, Guoqiang & Qi, Chengying & Wu, Xiangdong, 2021. "A dynamic control strategy of district heating substations based on online prediction and indoor temperature feedback," Energy, Elsevier, vol. 235(C).
    9. Protić, Milan & Shamshirband, Shahaboddin & Anisi, Mohammad Hossein & Petković, Dalibor & Mitić, Dragan & Raos, Miomir & Arif, Muhammad & Alam, Khubaib Amjad, 2015. "Appraisal of soft computing methods for short term consumers' heat load prediction in district heating systems," Energy, Elsevier, vol. 82(C), pages 697-704.
    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. Benakopoulos, Theofanis & Tunzi, Michele & Salenbien, Robbe & Hansen, Kasper Klan & Svendsen, Svend, 2022. "Implementation of a strategy for low-temperature operation of radiator systems using data from existing digital heat cost allocators," Energy, Elsevier, vol. 251(C).
    2. Tunzi, Michele & Benakopoulos, Theofanis & Yang, Qinjiang & Svendsen, Svend, 2023. "Demand side digitalisation: A methodology using heat cost allocators and energy meters to secure low-temperature operations in existing buildings connected to district heating networks," Energy, Elsevier, vol. 264(C).
    3. Zhongbo Li & Zheng Luo & Ning Zhang & Xiaojie Lin & Wei Huang & Encheng Feng & Wei Zhong, 2023. "Investigation of Predictive Regulation Strategy of Secondary Loop in District Heating Systems," Sustainability, MDPI, vol. 15(4), pages 1-15, February.

    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. 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).
    2. 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).
    3. 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).
    4. 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).
    5. Yuan, Jianjuan & Wang, Chendong & Zhou, Zhihua, 2019. "Study on refined control and prediction model of district heating station based on support vector machine," Energy, Elsevier, vol. 189(C).
    6. 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.
    7. Wang, Chendong & Yuan, Jianjuan & Zhang, Ji & Deng, Na & Zhou, Zhihua & Gao, Feng, 2020. "Multi-criteria comprehensive study on predictive algorithm of heating energy consumption of district heating station based on timeseries processing," Energy, Elsevier, vol. 202(C).
    8. Wang, Yanmin & Li, Zhiwei & Liu, Junjie & Pei, Mingzhe & Zhao, Yan & Lu, Xuan, 2023. "Data-driven analysis and prediction of indoor characteristic temperature in district heating systems," Energy, Elsevier, vol. 282(C).
    9. 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).
    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. Zhang, Yunfei & Zhou, Zhihua & Liu, Junwei & Yuan, Jianjuan, 2022. "Data augmentation for improving heating load prediction of heating substation based on TimeGAN," Energy, Elsevier, vol. 260(C).
    12. Huang, Ke & Yuan, Jianjuan & Zhou, Zhihua & Zheng, Xuejing, 2022. "Analysis and evaluation of heat source data of large-scale heating system based on descriptive data mining techniques," Energy, Elsevier, vol. 251(C).
    13. 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).
    14. Yuan, Jianjuan & Zhou, Zhihua & Huang, Ke & Han, Zhao & Wang, Chendong & Lu, Shilei, 2021. "Analysis and evaluation of the operation data for achieving an on-demand heating consumption prediction model of district heating substation," Energy, Elsevier, vol. 214(C).
    15. 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.
    16. Zhong, Wei & Feng, Encheng & Lin, Xiaojie & Xie, Jinfang, 2022. "Research on data-driven operation control of secondary loop of district heating system," Energy, Elsevier, vol. 239(PB).
    17. 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.
    18. Liu, Zhikai & Zhang, Huan & Wang, Yaran & You, Shijun & Dai, Ting & Jiang, Yan, 2024. "Evaluation of the controllability of multi-family building with radiator heating systems: A frequency domain approach," Energy, Elsevier, vol. 294(C).
    19. 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).
    20. Vogler–Finck, P.J.C. & Bacher, P. & Madsen, H., 2017. "Online short-term forecast of greenhouse heat load using a weather forecast service," Applied Energy, Elsevier, vol. 205(C), pages 1298-1310.

    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:245:y:2022:i:c:s0360544222001177. 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.