Fuzzy cognitive maps based decision support system for renewable energy investments

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dc.authorscopusid57197235590
dc.authorscopusid26656037500
dc.authorscopusid56524647100
dc.contributor.authorHasan, D.
dc.contributor.authorSağbaş, Aysun
dc.contributor.authorÇapraz, Ozan
dc.date.accessioned2022-05-11T14:26:33Z
dc.date.available2022-05-11T14:26:33Z
dc.date.issued2014
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü
dc.descriptionComputer and Industrial Engineering;et al.;Gaziantep University;Istanbul Commercial University;Journal of Intelligent Manufacturing Systems;Sakarya University, Department of Industrial Engineering
dc.descriptionJoint International Symposium on "The Social Impacts of Developments in Information, Manufacturing and Service Systems" 44th International Conference on Computers and Industrial Engineering, CIE 2014 and 9th International Symposium on Intelligent Manufacturing and Service Systems, IMSS 2014 -- 14 October 2014 through 16 October 2014 -- -- 110500
dc.description.abstractToday, renewable energy sources such as hydropower, biomass, geothermal, solar and wind play a major role in meeting the energy requirements in terms of economics and politics aspects for any country. The selection of the optimum renewable energy source provides guidelines to practitioners for sustainable planning of local, national, regional and global energy systems. The selection process is a multi-criteria decision making problem and requires taking into account social, technological, economic, environmental, and political aspects. In this study, a decision support system consisting of combined Fuzzy Cognitive Mapping (FCM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology is proposed for the planning of national renewable energy investments of the developing countries. FCM draws cognitive maps which intend to model the different aspects of the behavior of complex systems. Therefore, FCM method is applied to explore and prioritize the critical factors which are likely to influence renewable energy investment decisions. Also, several maps obtained from the experts who have diverse backgrounds in the public and private sectors are integrated into a map. Then, TOPSIS method is applied to rank renewable energy alternatives and select best one. In order to demonstrate the usefulness of the proposed methodology, an illustrated example of Turkey is performed.
dc.identifier.endpage1705
dc.identifier.scopus2-s2.0-84923886583
dc.identifier.startpage1693
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6472
dc.indekslendigikaynakScopus
dc.institutionauthorHasan, D.
dc.institutionauthorSağbaş, Aysun
dc.institutionauthorÇapraz, Ozan
dc.language.isoen
dc.publisherComputers and Industrial Engineering
dc.relation.ispartofCIE 2014 - 44th International Conference on Computers and Industrial Engineering and IMSS 2014 - 9th International Symposium on Intelligent Manufacturing and Service Systems, Joint International Symposium on "The Social Impacts of Developments in Information, Manufacturing and Service Systems" - Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDecision support system
dc.subjectFuzzy cognitive mapping
dc.subjectRenewable energy
dc.subjectTopsis
dc.subjectArtificial intelligence
dc.subjectCognitive systems
dc.subjectDecision making
dc.subjectDecision support systems
dc.subjectDeveloping countries
dc.subjectEconomics
dc.subjectEnergy policy
dc.subjectGeothermal energy
dc.subjectLarge scale systems
dc.subjectManufacture
dc.subjectMapping
dc.subjectNatural resources
dc.subjectRenewable energy resources
dc.subjectSustainable development
dc.subjectFuzzy cognitive mappings
dc.subjectMulti-criteria decision making problems
dc.subjectPublic and private sector
dc.subjectRenewable energies
dc.subjectRenewable energy investments
dc.subjectRenewable energy source
dc.subjectTechnique for order preference by similarity to ideal solutions
dc.subjectTopsis
dc.subjectInvestments
dc.titleFuzzy cognitive maps based decision support system for renewable energy investments
dc.typeConference Object

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