Selection of wind turbines with multi-criteria decision making techniques involving neutrosophic numbers: A case from Turkey. (15th September 2020)

• Record Type: Journal Article
• Authors: Supciller, Aliye Ayca; Toprak, Fatih
• Is Part Of: Energy. Volume 207(2020)
• Publication Date: 2020-09-15
• Subjects: Wind turbine -- Decision -- Neutrosophic number -- SWARA -- TOPSIS -- EDAS; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118237
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Countries that supply the majority of their energy needs from fossil fuels are turning to new, renewable, and eco-friendly energy sources. Wind energy technology has been the most promising alternative to traditional energy systems. The aim of this case study was the selection of the best wind turbine for one of the leading companies in Turkey. First, the studies on wind turbines in the literature were reviewed. The criteria used in these studies were listed. Interviews were conducted with the experts from the company and 21 criteria and the turbine alternatives were determined. Because there are multiple criteria, and some of them conflict with each other, it has been preferable to use multi-criteria decision making (MCDM) methods. The criteria were weighted by the SWARA method, which was not used in the previous turbine studies. The criterion weights of alternatives were given by experts using linguistic variable-defined neutrosophic numbers for the first time to select a wind turbine. TOPSIS and EDAS methods were used as the solution methods integrated with single valued neutrosophic numbers. The best wind turbine was selected with the assistance of the aggregation method of Borda. Highlights: A novel method for selecting the appropriate wind turbine for a company in Turkey. SWARA to determine the weights of criteria. Single-valued neutrosophic numbers (SVNS) for the uncertainties in selection of wind turbines. Integration of SWARA with SVNS-TOPSIS and SVNS-EDAS to rank the wind turbines.