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

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  • 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
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