Evaluation of sustainable energy systems in smart cities using a Multi-Expert Pythagorean fuzzy BWM & TOPSIS methodology

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dc.authorscopusid56008026000
dc.authorscopusid57773183300
dc.authorscopusid8572344300
dc.authorscopusid7003388495
dc.contributor.authorOtay, İ.
dc.contributor.authorÇevik Onar, S.
dc.contributor.authorÖztayşi, B.
dc.contributor.authorKahraman, C.
dc.date.accessioned2024-07-18T20:16:49Z
dc.date.available2024-07-18T20:16:49Z
dc.date.issued2024
dc.description.abstractSmart cities are technological settlements using the collected data to utilize resources and services effectively by combining information and communication technologies with various tools connected to the internet of things network. Sustainable energy systems in smart cities are systems can be evaluated by using multiple criteria decision making methods or methodologies based on several vague/imprecise evaluation criteria. In this paper, sustainable energy systems in smart cities are evaluated by interval-valued Pythagorean fuzzy (IVPF) sets with an integrated optimization based multi-expert fuzzy Best Worst Method (BWM) and TOPSIS methodology that can better handle uncertainty and vagueness in experts’ linguistic assessments than existing methodologies. The considered criteria are weighted by multi-expert IVPF Best Worst Method, which has become a popular weighting method in recent years. Later, the energy alternatives for a real case study are prioritized by multi-expert IVPF TOPSIS method. In the analysis, the most important criterion is found as Environmental sustainability (C1) with the defuzzified weight of 0.218 while the other weights are as initial investment (C2) with 0.196, operating expenses (C3) with 0.163, technical feasibility (C4) with 0.154, social acceptability (C5) with 0.140, and scalability (C6) with 0.129. The obtained results indicate that “Investing in advanced technologies” in a smart city with relative degree of closeness (RDC) value of 0.798, has been determined as the best alternative among the considered five alternatives. It is closely followed by “Developing a transportation system” with the RDC value of 0.681. Sensitivity analysis shows that the ranking results are quite robust and reliable. The comparative analysis with crisp BWM and TOPSIS methodology is applied to check the validity of the proposed methodology. © 2024 Elsevier Ltden_US
dc.identifier.doi10.1016/j.eswa.2024.123874
dc.identifier.issn0957-4174
dc.identifier.scopus2-s2.0-85189856382en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2024.123874
dc.identifier.urihttps://hdl.handle.net/11411/6286
dc.identifier.volume250en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInterval-Valued Pythagorean Fuzzy Setsen_US
dc.subjectMulti-Expert Fuzzy Mcdmen_US
dc.subjectPythagorean Fuzzy Best Worst Methoden_US
dc.subjectPythagorean Fuzzy Topsısen_US
dc.subjectSmart Citiesen_US
dc.subjectSustainable Energy Systemsen_US
dc.subjectDecision Makingen_US
dc.subjectEnergy Conservationen_US
dc.subjectEnergy Policyen_US
dc.subjectInvestmentsen_US
dc.subjectSensitivity Analysisen_US
dc.subjectSustainable Developmenten_US
dc.subjectBad Methodsen_US
dc.subjectFuzzy Mcdmen_US
dc.subjectFuzzy-Topsısen_US
dc.subjectInterval-Valueden_US
dc.subjectInterval-Valued Pythagorean Fuzzy Seten_US
dc.subjectMulti-Experten_US
dc.subjectMulti-Expert Fuzzy Mcdmen_US
dc.subjectPythagorean Fuzzy Best Bad Methoden_US
dc.subjectPythagorean Fuzzy Topsısen_US
dc.subjectSustainable Energy Systemsen_US
dc.subjectSmart Cityen_US
dc.titleEvaluation of sustainable energy systems in smart cities using a Multi-Expert Pythagorean fuzzy BWM & TOPSIS methodologyen_US
dc.typeArticleen_US

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