IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v280y2023ics0360544223015189.html
   My bibliography  Save this article

Identifying supply-demand mismatches in district heating system based on association rule mining

Author

Listed:
  • Sun, Chunhua
  • Yuan, Lingyu
  • Cao, Shanshan
  • Xia, Guoqiang
  • Liu, Yanan
  • Wu, Xiangdong

Abstract

Supply-demand mismatches in district heating system (DHS) may affect system's safe and stable operation without reasonable regulation actions in time. The accurate identification of supply-demand matching states is the prerequisite for heat balance regulation. However, this job is quite difficult and time-consuming, as DHS is typically a large time delay system and operates under various conditions. This paper proposes an association rule mining-based method to identify supply-demand mismatch from DHS operation data. Considering DHS's thermal inertia and fault-tolerance, the supply-demand mismatch rate is firstly defined along with its quantitative evaluation model. Then the theoretical intervals of the heating parameters are obtained by establishing association rules and rules post-processing. And a reasonable error band is decided by comparing the occurrence times of mismatches to balance identification accuracy and sensitivity. The proposed method is tested and verified in a typical DHS. The rules between primary supply temperature and relevant operation parameters are mined. An error band of ±7.5% is determined by comparison with historical operation data. The supply-demand mismatch rate is calculated to be −10%–15%. The identification results are validated by analyzing the heat substations' operation data including supply and return temperature, flow rate, and valve opening.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:280:y:2023:i:c:s0360544223015189
    DOI: 10.1016/j.energy.2023.128124
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223015189
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.128124?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. Xue, Puning & Zhou, Zhigang & Fang, Xiumu & Chen, Xin & Liu, Lin & Liu, Yaowen & Liu, Jing, 2017. "Fault detection and operation optimization in district heating substations based on data mining techniques," Applied Energy, Elsevier, vol. 205(C), pages 926-940.
    2. Zhao, Xiaoli & Zhang, Sufang & Yang, Rui & Wang, Mei, 2012. "Constraints on the effective utilization of wind power in China: An illustration from the northeast China grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4508-4514.
    3. Sun, Chunhua & Liu, Yiting & Cao, Shanshan & Chen, Jiali & Xia, Guoqiang & Wu, Xiangdong, 2022. "Identification of control regularity of heating stations based on cross-correlation function dynamic time delay method," Energy, Elsevier, vol. 246(C).
    4. Li, Guannan & Hu, Yunpeng & Chen, Huanxin & Li, Haorong & Hu, Min & Guo, Yabin & Liu, Jiangyan & Sun, Shaobo & Sun, Miao, 2017. "Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions," Applied Energy, Elsevier, vol. 185(P1), pages 846-861.
    5. 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).
    6. 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).
    7. 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).
    8. 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).
    9. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    10. Walsh, Angélica & Cóstola, Daniel & Labaki, Lucila Chebel, 2022. "Performance-based climatic zoning method for building energy efficiency applications using cluster analysis," Energy, Elsevier, vol. 255(C).
    11. Hirsch, Hauke & Nicolai, Andreas, 2022. "An efficient numerical solution method for detailed modelling of large 5th generation district heating and cooling networks," Energy, Elsevier, vol. 255(C).
    12. Kang, Ligai & Yuan, Xiaoxue & Sun, Kangjie & Zhang, Xu & Zhao, Jun & Deng, Shuai & Liu, Wei & Wang, Yongzhen, 2022. "Feed-forward active operation optimization for CCHP system considering thermal load forecasting," Energy, Elsevier, vol. 254(PB).
    13. Finkenrath, Matthias & Faber, Till & Behrens, Fabian & Leiprecht, Stefan, 2022. "Holistic modelling and optimisation of thermal load forecasting, heat generation and plant dispatch for a district heating network," Energy, Elsevier, vol. 250(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. Huang, Ke & Lu, Shilei & Han, Zhao & Yuan, Jianjuan, 2023. "Research on heat consumption detection, restoration and prediction methods for discontinuous heating substation," Energy, Elsevier, vol. 266(C).
    16. 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).
    17. Wang, Haichao & Hua, Pengmin & Wu, Xiaozhou & Zhang, Ruoyu & Granlund, Katja & Li, Ji & Zhu, Yingjie & Lahdelma, Risto & Teppo, Esa & Yu, Li, 2022. "Heat-power decoupling and energy saving of the CHP unit with heat pump based waste heat recovery system," Energy, Elsevier, vol. 250(C).
    18. Zhang, Chaobo & Xue, Xue & Zhao, Yang & Zhang, Xuejun & Li, Tingting, 2019. "An improved association rule mining-based method for revealing operational problems of building heating, ventilation and air conditioning (HVAC) systems," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    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. 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).

    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. Huang, Ke & Lu, Shilei & Han, Zhao & Yuan, Jianjuan, 2023. "Research on heat consumption detection, restoration and prediction methods for discontinuous heating substation," Energy, Elsevier, vol. 266(C).
    2. 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).
    3. 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).
    4. 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).
    5. 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).
    6. 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).
    7. 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).
    8. 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).
    9. Fan, Cheng & Xiao, Fu & Zhao, Yang & Wang, Jiayuan, 2018. "Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data," Applied Energy, Elsevier, vol. 211(C), pages 1123-1135.
    10. Hong, Yejin & Yoon, Sungmin, 2022. "Holistic Operational Signatures for an energy-efficient district heating substation in buildings," Energy, Elsevier, vol. 250(C).
    11. Fan, Cheng & Sun, Yongjun & Shan, Kui & Xiao, Fu & Wang, Jiayuan, 2018. "Discovering gradual patterns in building operations for improving building energy efficiency," Applied Energy, Elsevier, vol. 224(C), pages 116-123.
    12. 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).
    13. Elsa Chaerun Nisa & Yean-Der Kuan & Chin-Chang Lai, 2021. "Chiller Optimization Using Data Mining Based on Prediction Model, Clustering and Association Rule Mining," Energies, MDPI, vol. 14(20), pages 1-14, October.
    14. Zhang, Chaobo & Xue, Xue & Zhao, Yang & Zhang, Xuejun & Li, Tingting, 2019. "An improved association rule mining-based method for revealing operational problems of building heating, ventilation and air conditioning (HVAC) systems," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    15. 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).
    16. 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).
    17. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    18. Zhao, Xiaoli & Li, Shujie & Zhang, Sufang & Yang, Rui & Liu, Suwei, 2016. "The effectiveness of China's wind power policy: An empirical analysis," Energy Policy, Elsevier, vol. 95(C), pages 269-279.
    19. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    20. Ming, Zeng & Lilin, Peng & Qiannan, Fan & Yingjie, Zhang, 2016. "Trans-regional electricity transmission in China: Status, issues and strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 572-583.

    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:280:y:2023:i:c:s0360544223015189. 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.