Research on Evaluation of Operation Data Quality of Intelligent District Heating Systems

Quantitative operational data mining and control strategies are essential for energy-saving regulation in intelligent District Heating Systems (DHSs). However, due to complex industrial environments and heterogeneous sensor networks, operational heating data often face a “zero ground-truth labels” bottleneck, rendering conventional residual-based unsupervised quality control algorithms ineffective without calibration baselines. To resolve this, this paper proposes an unsupervised multivariate data quality assessment framework constrained jointly by physical and statistical topologies. The framework evaluates time-series data streams across five dimensions: completeness, typicality, consistency, uniqueness, and timeliness. At the statistical topology level, the Mahalanobis distance identifies the spatiotemporal distribution center of multivariate variables, replacing traditional accuracy metrics with statistical typicality to enable self-consistent quantification without ground truth. At the physical topology level, coupled logical relations between primary and secondary heating networks are extracted as rigid first-principles constraints. An information entropy weight method then adaptively determines indicator weights to eliminate subjective biases. Full-sample validation was conducted using real-world SCADA data across a complete heating season from a regional network zone (46 heat exchange stations). The network-wide average data quality score reached 0.905, confirming overall control-loop input readiness, though specific stations exhibited cascading degradation from localized physical faults. This framework systematically reveals data quality heterogeneity in complex DHS and provides a generalizable theoretical baseline for Industrial Internet of Things (IIoT) data cleansing under zero-ground-truth conditions.