A machine learning approach to fault detection in district heating substations

• Published in: 16th 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
• Language: English
• 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

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...