A machine learning approach to fault detection in district heating substations

The aim of this study is to develop a model capable of predicting the behavior of a district heating substation, including being able to distinguish datasets from well performing substations from datasets containing faults. The model developed in the study is based on machine learning algorithms and...

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Bibliographic Details
Published in16th International Symposium on District Heating and Cooling, DHC 2018,Hamburg, Germany,2018-09-09 - 2018-09-12 Vol. 149; pp. 226 - 235
Main Authors Månsson, Sara, Kallioniemi, Per-Olof Johansson, Sernhed, Kerstin, Thern, Marcus
Format Journal Article Conference Proceeding
LanguageEnglish
Published Elsevier Ltd 01.01.2018
Subjects
District heating substations
Energisystem
Energy Systems
Engineering and Technology
Environmental Engineering
fault detection
machine learning
Naturresursteknik
Teknik
machine learning
District heating substations
fault detection
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Summary:The aim of this study is to develop a model capable of predicting the behavior of a district heating substation, including being able to distinguish datasets from well performing substations from datasets containing faults. The model developed in the study is based on machine learning algorithms and the model is trained on data from a Swedish district heating substation. A number of different models and input/output parameters are tested in the study. The results show that the model is capable of modelling the substation behavior, and that the fault detection capability of the model is high.
ISSN:1876-6102
1876-6102
DOI:10.1016/j.egypro.2018.08.187