Comparison and Optimization of Machine Learning Methods for Fault Detection in District Heating and Cooling Systems

dc.contributor.author Cinar, Mehmet
dc.contributor.author Aslan, Emrah
dc.contributor.author Ozupak, Yildirim
dc.date.accessioned 2025-05-01T22:15:35Z
dc.date.accessioned 2025-09-17T14:28:26Z
dc.date.available 2025-05-01T22:15:35Z
dc.date.available 2025-09-17T14:28:26Z
dc.date.issued 2025
dc.description.abstract In this study, the methods used for the detection of sub-station pollution failures in district heating and cooling (DHC) systems are analyzed. In the study, high, medium, and low-level pollution situations are considered and machine learning methods are applied for the detection of these failures. Random forest, decision tree, logistic regression, and CatBoost regression algorithms are compared within the scope of the analysis. The models are trained to perform fault detection at different pollution levels. To improve the model performance, hyper parameter optimization was performed with random search optimization, and the most appropriate values were selected. The results show that the CatBoost regression algorithm provides the highest accuracy and overall performance compared to other methods. The CatBoost model stood out with an accuracy of 0.9832 and a superior performance. These findings reveal that CatBoost-based approaches provide an effective solution in situations requiring high accuracy, such as contamination detection in DHC systems. The study makes an important contribution as a reliable fault detection solution in industrial applications. en_US
dc.identifier.doi 10.24425/bpasts.2025.154063
dc.identifier.issn 0239-7528
dc.identifier.issn 2300-1917
dc.identifier.scopus 2-s2.0-105000172181
dc.identifier.uri https://doi.org/10.24425/bpasts.2025.154063
dc.identifier.uri https://hdl.handle.net/20.500.12514/9607
dc.language.iso en en_US
dc.publisher Polska Akad Nauk, Polish Acad Sci, Div IV Technical Sciences PAS en_US
dc.relation.ispartof Bulletin of the Polish Academy of Sciences-Technical Sciences en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Pollution Detection en_US
dc.subject Grid Search Optimization en_US
dc.subject Machine Learning en_US
dc.subject DHC en_US
dc.title Comparison and Optimization of Machine Learning Methods for Fault Detection in District Heating and Cooling Systems en_US
dc.type Article en_US
dspace.entity.type Publication

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