Analyzing energy transition for industry 4.0-driven hybrid energy system selection with advanced neural network-used multi-criteria decision-making technique

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dc.authorid0000-0002-4791-4091
dc.authorid0000-0002-9858-1266
dc.authorid0000-0002-8072-031X
dc.authorid0000-0002-3390-4597
dc.authorid0000-0003-2212-287X
dc.contributor.authorLiu, Peide
dc.contributor.authorEti, Serkan
dc.contributor.authorYüksel, Serhat
dc.contributor.authorDinçer, Hasan
dc.contributor.authorGökalp, Yaşar
dc.contributor.authorErgün, Edanur
dc.contributor.authorAysan, Ahmet Faruk
dc.date.accessioned2024-09-24T12:51:31Z
dc.date.available2024-09-24T12:51:31Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, İMÜ Meslek Yüksekokulu, Bilgisayar Programcılığı Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Uluslararası Ticaret ve Finansman Bölümü
dc.departmentİstanbul Medipol Üniversitesi, Sağlık Bilimleri Fakültesi, Sağlık Yönetimi Bölümü
dc.description.abstractThis study aims to select the appropriate renewable energy alternatives for the efficiency of hybrid energy systems to increase energy transition performance. For this purpose, a novel neural network (NN)-based fuzzy decision-making model is constructed that has three different stages. In the first stage, NN-based fuzzy decision matrix is created. Secondly, 6 different variables based on industry 4.0 are weighted with the sine trigonometric Pythagorean fuzzy entropy technique. Additionally, another calculation has been implemented with criteria importance through intercriteria correlation (CRITIC) to identify the consistency of the results. Furthermore, in the third stage, considering 5 different renewable energy alternatives, 10 different combinations are identified for hybrid energy systems. The most effective alternatives are defined by the sine trigonometric Pythagorean fuzzy ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS) method. Moreover, to test the validity of these results, another analysis is conducted using the additive ratio assessment (ARAS) technique. The main contribution of the study is that the optimal renewable energy combination required for an efficient hybrid energy system is determined by performing a priority analysis between the variables. This situation has a significant guiding feature for investors. Similarly, the development of the RATGOS technique both increases the methodological originality of the study and enables more accurate alternative ranking. It is identified that the results of all methods are similar. Therefore, this situation gives information about the coherency and validity of the findings. It is concluded that the most important criterion is real-time capability. It is also denoted that the best combination for hybrid energy systems is Solar-Wind.
dc.identifier.citationLiu, P., Eti, S., Yüksel, S., Dinçer, H., Gökalp, Y., Ergün, E. ... Aysan, A. F. (2024). Analyzing energy transition for industry 4.0-driven hybrid energy system selection with advanced neural network-used multi-criteria decision-making technique. Renewable Energy, 232. http://dx.doi.org/10.1016/j.renene.2024.121081
dc.identifier.doi10.1016/j.renene.2024.121081
dc.identifier.issn0960-1481
dc.identifier.issn1879-0682
dc.identifier.scopus2-s2.0-85201204122
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.renene.2024.121081
dc.identifier.urihttps://hdl.handle.net/20.500.12511/12837
dc.identifier.volume232
dc.identifier.wos001296852300001en_US
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorEti, Serkan
dc.institutionauthorYüksel, Serhat
dc.institutionauthorDinçer, Hasan
dc.institutionauthorGökalp, Yaşar
dc.institutionauthorErgün, Edanur
dc.language.isoen
dc.relation.ispartofRenewable Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDecision-Making Models
dc.subjectEffective Resource Policies
dc.subjectFuzzy Logic
dc.subjectHybrid Energy Projects
dc.subjectRenewable Energy Investments
dc.titleAnalyzing energy transition for industry 4.0-driven hybrid energy system selection with advanced neural network-used multi-criteria decision-making technique
dc.typeArticle

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