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A machine learning approach to fault detection in district heating substations

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Author

Summary, in English

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.

Publishing year

2018

Language

English

Pages

226-235

Publication/Series

Energy Procedia

Volume

149

Document type

Conference paper

Topic

  • Energy Systems

Keywords

  • District heating substations
  • fault detection
  • machine learning

Conference name

16th International Symposium on District Heating and Cooling, DHC 2018

Conference date

2018-09-09 - 2018-09-12

Conference place

Hamburg, Germany

Status

Published

ISBN/ISSN/Other

  • ISSN: 1876-6102