Solar PV power plant location selection using a Z-fuzzy number based AHP
dc.authorscopusid | 56008026000 | |
dc.authorscopusid | 7003388495 | |
dc.contributor.author | Otay,I. | |
dc.contributor.author | Kahraman,C. | |
dc.date.accessioned | 2024-05-25T12:32:36Z | |
dc.date.available | 2024-05-25T12:32:36Z | |
dc.date.issued | 2018 | |
dc.department | Okan University | en_US |
dc.department-temp | Otay I., Istanbul Okan University, Faculty of Engineering, Industrial Engineering Department, Akfirat-Tuzla, Istanbul, 34959, Turkey; Kahraman C., Istanbul Technical University, Faculty of Management, Industrial Engineering Department, Maçka, Istanbul, 34367, Turkey | en_US |
dc.description.abstract | One of the most used renewable energy systems to produce clean and sustainable energy are solar energy photovoltaic (PV) plants. The selection among solar energy PV plant location alternatives requires a multi-criteria decision making approach with several conflicting and linguistic criteria. The assessment process is generally done in a vague and imprecise environment. Fuzzy set theory is often very beneficial for evaluating the subjective judgments of decision makers. The Analytic Hierarchy Process is the most used multi-criteria decision making method in the world because of its simplicity and efficiency. In this paper, we select a location for a solar energy PV plant using a 4-level hierarchy. We consider several criteria and sub-criteria including initial cost, maintenance cost, slope and distance to highways. A Z-fuzzy number is a relatively new concept in fuzzy set theory that enables one to circumvent the limitations of ordinary fuzzy numbers. Z-fuzzy numbers can be viewed as a combination of crisp numbers, intervals, fuzzy numbers and random numbers because of their generality. They give a better representation than ordinary fuzzy numbers. This study solves the multi-criteria solar PV power plant location selection problem with a Z-fuzzy based AHP method. To check the applicability of the method proposed here, a real-life case study from Turkey is presented and solved. © 2018 International Journal of the Analytic Hierarchy Process. | en_US |
dc.identifier.citation | 25 | |
dc.identifier.doi | 10.13033/ijahp.v10i3.540 | |
dc.identifier.endpage | 430 | en_US |
dc.identifier.issn | 1936-6744 | |
dc.identifier.issue | 3 | en_US |
dc.identifier.scopus | 2-s2.0-85059232755 | |
dc.identifier.scopusquality | Q3 | |
dc.identifier.startpage | 409 | en_US |
dc.identifier.uri | https://doi.org/10.13033/ijahp.v10i3.540 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14517/2402 | |
dc.identifier.volume | 10 | en_US |
dc.language.iso | en | |
dc.publisher | Creative Decisions Foundation | en_US |
dc.relation.ispartof | International Journal of the Analytic Hierarchy Process | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Fuzzy AHP | en_US |
dc.subject | Location selection | en_US |
dc.subject | Multi-criteria | en_US |
dc.subject | Solar PV power plant | en_US |
dc.subject | Uncertainty | en_US |
dc.subject | Z-fuzzy number | en_US |
dc.title | Solar PV power plant location selection using a Z-fuzzy number based AHP | en_US |
dc.type | Article | en_US |
dspace.entity.type | Publication |