DETECTION OF SUBSTATION POLLUTION IN DISTRICT HEATING AND COOLING SYSTEMS: A COMPREHENSIVE COMPARATIVE ANALYSIS OF MACHINE LEARNING AND ARTIFICIAL NEURAL NETWORK MODELS

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dc.contributor.authorAslan, Emrah
dc.contributor.authorÖzüpak, Yıldırım
dc.date.accessioned2025-02-22T14:10:57Z
dc.date.available2025-02-22T14:10:57Z
dc.date.issued2024
dc.departmentDicle Üniversitesien_US
dc.description.abstractThis study analyzes the detection of substation fouling failures in District Heating and Cooling (DHC) systems using synthetic data. In the study, high, medium and low levels of contamination are considered and both machine learning and deep learning techniques are applied for the detection of these failure types. Within the scope of the analysis, machine learning algorithms such as K-Nearest Neighbors, XGBoost and AdaBoost are compared with the proposed Convolutional Neural Network (CNN) model. The machine learning algorithms and the Convolutional Neural Network model are trained to perform fault detection at different contamination levels. In order to improve the performance of the machine learning models, hyperparameter tuning was performed by Grid Search Optimization method. The results obtained show that the proposed Convolutional Neural Network model provides higher accuracy and overall success compared to machine learning methods. High performance measures such as Matthews correlation coefficient 0.944 and accuracy rate 0.972 were achieved with the CNN model. These findings reveal that contamination detection in substations can be done effectively with CNN-based approaches, especially for situations that require high accuracy. This study on fault detection in DHC systems provides a new and reliable solution for industrial applications. © 2024, Galileo Institute of Technology and Education of the Amazon (ITEGAM). All rights reserved.en_US
dc.identifier.doi10.5935/jetia.v10i50.1289
dc.identifier.endpage27en_US
dc.identifier.issn2447-0228
dc.identifier.issue50en_US
dc.identifier.scopus2-s2.0-85212523938en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage17en_US
dc.identifier.urihttps://doi.org/10.5935/jetia.v10i50.1289
dc.identifier.urihttps://hdl.handle.net/11468/29926
dc.identifier.volume10en_US
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherGalileo Institute of Technology and Education of the Amazon (ITEGAM)en_US
dc.relation.ispartofJournal of Engineering and Technology for Industrial Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_Scopus_20250222
dc.subjectCNNen_US
dc.subjectDHCen_US
dc.subjectGrid Search Optimizationen_US
dc.subjectMachine Learningen_US
dc.subjectPollution Detectionen_US
dc.titleDETECTION OF SUBSTATION POLLUTION IN DISTRICT HEATING AND COOLING SYSTEMS: A COMPREHENSIVE COMPARATIVE ANALYSIS OF MACHINE LEARNING AND ARTIFICIAL NEURAL NETWORK MODELSen_US
dc.typeArticleen_US

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