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

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 & PLUSMN;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.

Automated summary

This paper proposes an association rule mining-based method to identify supply-demand mismatch from DHS operation data. The proposed method is tested and verified in a typical DHS. The identification results are validated by analyzing the heat substations' operation data.