District Heating Substation Behaviour Modelling for Annotating the Performance

Abghari, Shahrooz; Boeva, Veselka; Brage, Jens; Johansson, Christian

doi:10.1007/978-3-030-43887-6_1

Cited by 1 publication

(4 citation statements)

References 10 publications

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“…Abghari et al [49][50][51] propose a DMKD using Higher Order Mining (HOM) to identify outliers for fault detection in DH systems. HOM is a sub-field of DMKD that uses non-primary, derived data or patterns [52].…”

Section: Data Mining and Knowledge Discoverymentioning

confidence: 99%

“…A majority of studies focus on clustering substations and utilize the kM algorithm with Euclidean distance as the similarity metric. However, some studies employ alternative distance metrics, such as those by [32,42,49]. Commonly used validation measures include DBI, SI, and BIC.…”

Section: Data Mining and Knowledge Discoverymentioning

confidence: 99%

“…The authors in [33] report better results using GMM instead of kM. There is some experimentation in using different techniques other than kM, such as in [32,42,[49][50][51]58]. For example, [32], uses kS, which has a similar iterative procedure seen in kM, but kS differs significantly in distance metric and centroid computation.…”

Section: Strengthsmentioning

confidence: 99%

“…The authors have reported good results and are successful in identifying deviating customers. In [49][50][51], the authors have used HOM and are successful in finding deviating substations. Consequently, a majority of DMKD studies utilize DBI or SI for clustering validation.…”

Section: Strengthsmentioning

confidence: 99%

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Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities

et al. 2023

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This paper presents a comprehensive survey of state-of-the-art intelligent fault detection and diagnosis in district heating systems. Maintaining an efficient district heating system is crucial, as faults can lead to increased heat loss, customer discomfort, and operational cost. Intelligent fault detection and diagnosis can help to identify and diagnose faulty behavior automatically by utilizing artificial intelligence or machine learning. In our survey, we review and discuss 57 papers published in the last 12 years, highlight the recent trends, identify current research gaps, discuss the limitations of current techniques, and provide recommendations for future studies in this area. While there is an increasing interest in the topic, and the past five years have shown much advancement, the absence of open-source high-quality labeled data severely hinders progress. Future research should aim to explore transfer learning, domain adaptation, and semi-supervised learning to improve current performance. Additionally, a researcher should increase knowledge of district heating data using data-centric approaches to establish a solid foundation for future fault detection and diagnosis in district heating.

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