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Fault and anomaly detection in district heating substations: A survey on methodology and data sets

  • District heating systems are essential building blocks for affordable, low-carbon heat supply. Early detection and elimination of faults is crucial for the efficiency of these systems and necessary to achieve the low temperatures targeted for 4th generation district heating systems. Especially methods for fault and anomaly detection in district heating substations are currently of high interest, as faults in substations can be repaired quickly and inexpensively, and smart meter data are becoming widely available. In this paper, we review recent scientific publications presenting data-driven approaches for fault and anomaly detection in district heating substations with a focus on methods and data sets. Our review indicates that researchers use a wide variety of methods, mostly focusing on unsupervised anomaly detection rather than fault detection. This is due to a lack of labeled data sets, preventing the use of supervised learning methods and quantitative analysis. Together with the lack of publicly available data sets, this impedes the accurate comparison of individual methods. To overcome this impediment, increase the comparability of different methods and foster competition, future research should focus on establishing publicly available data sets, and industry-relevant metrics as benchmarks.

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Author:Martin Neumayer, Dominik Stecher, Sebastian Grimm, Andreas Maier, Dominikus Bücker, Jochen Schmidt
DOI:https://doi.org/10.1016/j.energy.2023.127569
Parent Title (English):Energy
Document Type:Article (peer reviewed)
Language:English
Publication Year:2023
Tag:District heating systems; Fault/anomaly detection; Machine learning
Volume:276
First Page:127569
Peer reviewed:Ja
Project Title:Intelligente Lernende Systeme in Energieverbünden
Acronym:ILSE
Funding Agency:BMWK
Grant Number:03EN3033
faculties / departments:Fakultät für Informatik
Fakultät für Ingenieurwissenschaften
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 05 Zeitschriften, fortlaufende Sammelwerke