Development of Temperature Control in Climatic Test Chambers with LSTM-based Deep Neural Network Algorithm

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dc.contributor.authorCakiroglu, Abdullah
dc.contributor.authorBayrak, Gökay
dc.contributor.authorNurel, Ayberk
dc.date.accessioned2026-02-12T21:02:48Z
dc.date.available2026-02-12T21:02:48Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023 -- 2023-06-08 through 2023-06-10 -- Istanbul -- 190025
dc.description.abstractEnvironmentally conditioned climatic test chambers are test devices that can simulate temperature and relative humidity conditions in a wide range as standard and can be produced in various volumes. PID is the most used control method in climatic chambers. Since the parameters in the PID controller are determined for wide ranges, the system performance is insufficient since the parameters at intermediate values and different volumes need to be optimized, long waiting times and high energy consumption to reach the target temperature value may cause the PID controller to not fully meet the requirements under some conditions. This study presents an innovative method for the control of interior space heating system with LSTM (Long-Short Time Memory)-based deep neural network model in accordance with the created test recipe. The model is trained with the dataset created with the outputs of the PID controller. As a result of the method used, an error value of 0.0014 was obtained. The presented results show that the trained model successfully predicts the outputs of the heating system according to the target temperatures. © 2023 IEEE.
dc.description.sponsorshipDipartimento di Scienze, Università degli Studi Roma Tre; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK, (119C145)
dc.identifier.doi10.1109/HORA58378.2023.10156695
dc.identifier.isbn9798350337525
dc.identifier.scopus2-s2.0-85165723467
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/HORA58378.2023.10156695
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6550
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofHORA 2023 - 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.snmzKA_Scopus_20260212
dc.subjectclimatic test chamber
dc.subjectdeep learning
dc.subjectLSTM
dc.subjectneural network
dc.subjectPID
dc.subjecttemperature control
dc.titleDevelopment of Temperature Control in Climatic Test Chambers with LSTM-based Deep Neural Network Algorithm
dc.typeConference Object

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