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Analysis and evaluation of heat source data of large-scale heating system based on descriptive data mining techniques

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  • Huang, Ke
  • Yuan, Jianjuan
  • Zhou, Zhihua
  • Zheng, Xuejing

Abstract

The on-demand parameters of heat source are the precondition for ensuring the safe, stable and energy-saving operation of heating system. For large-scale heating system, the existing predictive methods are not applicable due to the complexity of modeling, while the design heating load index method has many influencing factors, resulting in a low accuracy of obtaining accurate values. In this paper, firstly, the simplified mathematical model of the heating substation is built, and the calculation methods of under-demand rate (η) and energy-saving rate (ς) are proposed for diagnosis thermal balance and evaluation energy-saving potential. Secondly, the variation relationship among heat source parameters is analyzed and the input parameters of the analysis process are determined, then cluster analysis is adopted to identify the operation strategy, and non-on-demand clusters are eliminated from the perspective of professional knowledge. Thirdly, the data in the remaining clusters are discretized, association analysis is used to obtain the frequent item-sets of each cluster, and on-demand heating parameters of each cluster are obtained. Finally, η and ς are used to evaluate the object heating system. This application reveals that the descriptive data mining techniques combined with professional knowledge can successfully identify the on-demand parameters from the historical data of heat source.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s036054422200737x
    DOI: 10.1016/j.energy.2022.123834
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    1. Dotzauer, Erik, 2002. "Simple model for prediction of loads in district-heating systems," Applied Energy, Elsevier, vol. 73(3-4), pages 277-284, November.
    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. Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio Sánchez-Esguevillas, 2012. "Classification and Clustering of Electricity Demand Patterns in Industrial Parks," Energies, MDPI, vol. 5(12), pages 1-14, December.
    4. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    5. Zheng, Xinye & Wei, Chu & Qin, Ping & Guo, Jin & Yu, Yihua & Song, Feng & Chen, Zhanming, 2014. "Characteristics of residential energy consumption in China: Findings from a household survey," Energy Policy, Elsevier, vol. 75(C), pages 126-135.
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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).
    12. 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.
    13. Wang, Yaran & You, Shijun & Zhang, Huan & Zheng, Wandong & Zheng, Xuejing & Miao, Qingwei, 2017. "Hydraulic performance optimization of meshed district heating network with multiple heat sources," Energy, Elsevier, vol. 126(C), pages 603-621.
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