A machine learning and fuzzy logic model for optimizing digital transformation in renewable energy: insights into industrial information integration

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dc.contributor.authorEti, Serkan
dc.contributor.authorYüksel, Serhat
dc.contributor.authorDinçer, Hasan
dc.contributor.authorPamucar, Dragan
dc.contributor.authorDeveci, Muhammet
dc.contributor.authorOlaru, Gabriela Oana
dc.date.accessioned2025-11-20T07:55:35Z
dc.date.available2025-11-20T07:55:35Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, İMÜ Meslek Yüksekokulu, Bilgisayar Programcılığı Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, İletişim Fakültesi, Yeni Medya ve İletişim Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Uluslararası Ticaret ve Finansman Bölümü
dc.description.abstractThe most essential criteria to improve digital transformation in renewable energy projects should be identified. This situation helps the companies to use limited financial budgets and human resources in the most efficient way. Therefore, a new study is needed to analyze the performance indicators of the digital transformation process in renewable energy projects. Accordingly, this study aims to identify the most significant performance indicators of digital transformation for these projects. A three-stage machine learning and fuzzy logic-based decision-making model has been constructed in this process. The first stage includes the weight calculation of the experts by dimension reduction methodology. Secondly, essential factors of digital transformation in renewable energy projects are examined via Fermatean fuzzy criteria importance through intercriteria correlation (CRITIC). The final part consists of the ranking of emerging seven countries with Fermatean fuzzy weighted aggregated sum product assessment (WASPAS). On the other side, combined compromise solution (CoCoSo) method is also taken into consideration in this process to make a comparative evaluation. The main contribution of this study is the generation of novel machine learning and fuzzy logic integrated decision-making model to make evaluation related to the digital transformation of renewable energy projects. In this model, machine learning technique is used to determine the importance weights of the experts. Similarly, integrating Fermatean fuzzy numbers with CRITIC and WASPAS techniques also contributes to the literature by minimizing the uncertainty and identifying the relationship between the items. The findings demonstrate that employing qualified personnel plays the most critical role in increasing digital transformation in renewable energy projects. Additionally, government support is very critical in the successful implementation of digital transformation processes in renewable energy projects.
dc.identifier.citationEti, S., Yüksel, S., Dinçer, H., Pamucar, D., Deveci, M. ve Olaru, G. O. (2024). A machine learning and fuzzy logic model for optimizing digital transformation in renewable energy: insights into industrial information integration. Journal of Industrial Information Integration, 42. http://dx.doi.org/10.1016/j.jii.2024.100734
dc.identifier.doi10.1016/j.jii.2024.100734
dc.identifier.issn2467-964X
dc.identifier.issn2452-414X
dc.identifier.scopus2-s2.0-85209110805
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.jii.2024.100734
dc.identifier.urihttps://hdl.handle.net/20.500.12511/13223
dc.identifier.volume42
dc.identifier.wosWOS:001360560500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorEti, Serkan
dc.institutionauthorYüksel, Serhat
dc.institutionauthorDinçer, Hasan
dc.institutionauthorOlaru, Gabriela Oana
dc.institutionauthorid0000-0002-4791-4091
dc.institutionauthorid0000-0002-9858-1266
dc.institutionauthorid0000-0002-8072-031X
dc.institutionauthorid0000-0001-9486-3349
dc.language.isoen
dc.relation.ispartofJournal of Industrial Information Integration
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCRITIC
dc.subjectDigital Transformation
dc.subjectFermatean Fuzzy Sets
dc.subjectMachine Learning
dc.subjectRenewable Energy Projects
dc.subjectWASPAS
dc.titleA machine learning and fuzzy logic model for optimizing digital transformation in renewable energy: insights into industrial information integration
dc.typeArticle

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