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A machine learning approach to fault detection in district heating substations

Månsson, Sara LU ; Kallioniemi, Per Olof Johansson LU ; Sernhed, Kerstin LU and Thern, Marcus LU (2018) 16th International Symposium on District Heating and Cooling, DHC 2018 In Energy Procedia 149. p.226-235
Abstract

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.

Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
District heating substations, fault detection, machine learning
host publication
16th International Symposium on District Heating and Cooling, DHC2018, 9–12 September 2018, Hamburg, Germany
series title
Energy Procedia
volume
149
pages
10 pages
conference name
16th International Symposium on District Heating and Cooling, DHC 2018
conference location
Hamburg, Germany
conference dates
2018-09-09 - 2018-09-12
external identifiers
  • scopus:85054087074
ISSN
1876-6102
DOI
10.1016/j.egypro.2018.08.187
language
English
LU publication?
yes
id
c4f2b0ac-7009-4f04-973b-6b7b36415938
date added to LUP
2018-10-22 14:49:45
date last changed
2019-01-06 14:11:39
@inproceedings{c4f2b0ac-7009-4f04-973b-6b7b36415938,
  abstract     = {<p>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.</p>},
  author       = {Månsson, Sara and Kallioniemi, Per Olof Johansson and Sernhed, Kerstin and Thern, Marcus},
  booktitle    = {Energy Procedia},
  issn         = {1876-6102},
  keyword      = {District heating substations,fault detection,machine learning},
  language     = {eng},
  location     = {Hamburg, Germany},
  pages        = {226--235},
  title        = {A machine learning approach to fault detection in district heating substations},
  url          = {http://dx.doi.org/10.1016/j.egypro.2018.08.187},
  volume       = {149},
  year         = {2018},
}