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Analyzing daily change patterns of indoor temperature in district heating systems: A clustering and regression approach

Author

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  • Wang, Yanmin
  • Li, Zhiwei
  • Liu, Junjie
  • Lu, Xuan
  • Zhao, Laifu
  • Zhao, Yan
  • Feng, Yongtao

Abstract

Measuring the indoor temperature of building rooms is a valuable approach for evaluating thermal comfort and providing feedback control for heat substations in district heating systems (DHSs) in China. Previous studies on indoor temperatures have primarily focused on analyzing their overall trends and influencing factors, while research on daily change patterns is lacking. This study utilized a clustering method to analyze the indoor temperature data from an actual DHS in Northeast China. First, a 24-h observation vector was constructed using the deviation between the actual and target values to represent the daily temperature pattern. Second, the k-means method was applied to cluster the values, and the quantity distribution and typical characteristics of each cluster were analyzed. Finally, a multi-nominal logistic regression model was used to analyze the influence of different factors on each cluster. The comparison results with the four representative clustering algorithms indicated that k-means was the optimal model and the optimal number of clusters was 4. The trend of each cluster was roughly the same, with the main difference being the fluctuation amplitude and distance from the target value. The differences between the clusters were related to various influencing features, with the primary return pressure for workdays and the secondary return pressure for holidays being the most significant. This study identified the optimal daily variation patterns of indoor temperature and analyzed the important features that affect this pattern, which is beneficial for enhancing the regulatory efficiency of DHS.

Suggested Citation

  • Wang, Yanmin & Li, Zhiwei & Liu, Junjie & Lu, Xuan & Zhao, Laifu & Zhao, Yan & Feng, Yongtao, 2024. "Analyzing daily change patterns of indoor temperature in district heating systems: A clustering and regression approach," Applied Energy, Elsevier, vol. 358(C).
    • Handle: RePEc:eee:appene:v:358:y:2024:i:c:s030626192400028x
    DOI: 10.1016/j.apenergy.2024.122645
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    References listed on IDEAS

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    1. 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).
    2. 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.
    3. Zhang, Qiang & Tian, Zhe & Ma, Zhijun & Li, Genyan & Lu, Yakai & Niu, Jide, 2020. "Development of the heating load prediction model for the residential building of district heating based on model calibration," Energy, Elsevier, vol. 205(C).
    4. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    5. Räsänen, Teemu & Voukantsis, Dimitrios & Niska, Harri & Karatzas, Kostas & Kolehmainen, Mikko, 2010. "Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data," Applied Energy, Elsevier, vol. 87(11), pages 3538-3545, November.
    6. González-Cabrera, Nestor & Ortiz-Bejar, Jose & Zamora-Mendez, Alejandro & Arrieta Paternina, Mario R., 2021. "On the Improvement of representative demand curves via a hierarchical agglomerative clustering for power transmission network investment," Energy, Elsevier, vol. 222(C).
    7. Wang, Chao & Du, Yuyan & Li, Hailong & Wallin, Fredrik & Min, Geyong, 2019. "New methods for clustering district heating users based on consumption patterns," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Xiong, Weiming & Wang, Yu & Mathiesen, Brian Vad & Lund, Henrik & Zhang, Xiliang, 2015. "Heat roadmap China: New heat strategy to reduce energy consumption towards 2030," Energy, Elsevier, vol. 81(C), pages 274-285.
    9. Ng, Selina S.Y. & Xing, Yinjiao & Tsui, Kwok L., 2014. "A naive Bayes model for robust remaining useful life prediction of lithium-ion battery," Applied Energy, Elsevier, vol. 118(C), pages 114-123.
    10. Zhong, Wei & Huang, Wei & Lin, Xiaojie & Li, Zhongbo & Zhou, Yi, 2020. "Research on data-driven identification and prediction of heat response time of urban centralized heating system," Energy, Elsevier, vol. 212(C).
    11. Gadd, Henrik & Werner, Sven, 2013. "Heat load patterns in district heating substations," Applied Energy, Elsevier, vol. 108(C), pages 176-183.
    12. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
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