Detecting and Evaluating Faults in Heating Systems
Author Information
Author(s): Samanta A. Weber, Michael Fischlschweiger, Dirk Volta, Ulf Rieck-Blankenburg
Primary Institution: University of Applied Sciences Flensburg
Hypothesis
Can a clustering and statistical approach effectively identify and evaluate faults in user substations of thermal energy systems?
Conclusion
The study successfully developed a workflow that automates fault detection and quantifies the impact of faults on heating system efficiency.
Supporting Evidence
- The study identified common fault indicators such as exceeded return temperature and very low cooling.
- Statistical methods confirmed the successful detection of faulty substations.
- The workflow allows for prioritization of fault elimination measures based on the quantified impact.
Takeaway
This study helps find problems in heating systems so they can work better and save energy.
Methodology
The study used a three-step workflow involving k-means clustering to identify fault indicators, followed by statistical methods to detect faulty substations and quantify their impact.
Potential Biases
Potential bias in expert labeling of fault indicators could affect the results.
Limitations
The study relies on data from a specific model region, which may not be generalizable to other areas.
Participant Demographics
The study focused on substations in a district heating network in northern Germany.
Digital Object Identifier (DOI)
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