Life performance prediction of natural gas combined cycle power plant with intelligent algorithms

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dc.contributor.authorKaraçor M.
dc.contributor.authorUysal A.
dc.contributor.authorMamur H.
dc.contributor.authorŞen G.
dc.contributor.authorNil M.
dc.contributor.authorBilgin M.Z.
dc.contributor.authorDoğan H.
dc.contributor.authorŞahin C.
dc.date.accessioned2025-04-10T11:05:16Z
dc.date.available2025-04-10T11:05:16Z
dc.date.issued2021
dc.description.abstractThe efficient use of a system is enabled with the life performance estimations. Thus, the effective use of underground resources is realized especially natural gas. Based on this, life performance models were generated to aim of improving the efficient use of energy for a combined cycle power plant (CCPP) of 243 MW installed in Izmir, Turkey by using fuzzy logic (FL) and artificial neural network (ANN) in this study. Therefore, output power estimations were carried out. Depending on the developed models, an estimation of the energy that the CCPP can produce and provide to the interconnected system in the following years has been made. According to the obtained results, the error prediction rates of FL and ANN models were determined. It was found that while the energy relative error estimation value that can be produced between the years calculated in modeling using FL varies between 0.59% and 3.54%, this value was found to vary between 0.001% and 0.84% in modeling using ANN. This result shows that the ANN model is more suitable for the life performance estimations of such a non-linear system. © 2021 Elsevier Ltd
dc.identifier.DOI-ID10.1016/j.seta.2021.101398
dc.identifier.urihttp://hdl.handle.net/20.500.14701/45919
dc.publisherElsevier Ltd
dc.titleLife performance prediction of natural gas combined cycle power plant with intelligent algorithms
dc.typeArticle

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