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Zeitschriftenbeiträge:

J. Malinao, F. Judex, T. Selke, G. Zucker, J. Caro, W. Kropatsch:
"Pattern mining and fault detection via CoP_therm-based profiling with correlation analysis of circuit variables in chiller systems";
Computer Science - Research and Development, 1 (2015), S. 1 - 9.



Kurzfassung:
In this paper, we propose methods of handling,
analyzing, and profiling monitoring data of energy systems
using their thermal coefficient of performance seen in uneven
segmentations in their time series databases. Aside from
assessing the performance of chillers using this parameter,
we dealt with pinpointing different trends that this parameter
undergoes through while the systems operate. From
these results, we identified and cross-validated with domain
experts outlier behavior which were ultimately identified as
faulty operation of the chiller. Finally, we establish correlations
of the parameter with the other independent variables
across the different circuits of the machine with or without
the observed faulty behavior.

Schlagworte:
Data mining · Energy efficiency ·Building automation · HVAC · Adsorption chiller

Erstellt aus der Publikationsdatenbank des AIT Austrian Institute of Technology.