.png){kind=icon}
.png){kind=icon}
Return
Challenges in detection of heating curve misconfiguration: An empirical study
.png){kind=icon}
OA
AI
Z
C
S
R
DOI：10.1016/j.segy.2026.100256.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Prophet analysis is unreliable across substations for heating curve diagnostics. • CAE detects abrupt faults but misses gradual heating curve misconfigurations. • Heating curve errors cause slow drifts that stay within normal temperature ranges. • Lack of operator intervention labels limits detection of misaligned heating curves.
Keywords:
District heating
Heating curve
Anomaly detection
Autoencoder
Fault detection
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5
Papers:
234
Citations:
641
Organization
Cited Papers