A machine learning approach to fault detection in district heating substations
(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:
https://lup.lub.lu.se/record/c4f2b0ac-7009-4f04-973b-6b7b36415938
- author
- Månsson, Sara LU ; Kallioniemi, Per Olof Johansson LU ; Sernhed, Kerstin LU and Thern, Marcus LU
- organization
- publishing date
- 2018
- 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
- 2022-04-17 23:28:33
@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 = {{16th International Symposium on District Heating and Cooling, DHC2018, 9–12 September 2018, Hamburg, Germany}}, issn = {{1876-6102}}, keywords = {{District heating substations; fault detection; machine learning}}, language = {{eng}}, pages = {{226--235}}, series = {{Energy Procedia}}, title = {{A machine learning approach to fault detection in district heating substations}}, url = {{http://dx.doi.org/10.1016/j.egypro.2018.08.187}}, doi = {{10.1016/j.egypro.2018.08.187}}, volume = {{149}}, year = {{2018}}, }