Development of a multi-stage Fermatean fuzzy decision support system for assessing solar power plant locations under uncertain conditions
Soft Computing, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s00500-026-11439-z
- Dergi Adı: Soft Computing
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Compendex, INSPEC, zbMATH, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Technology Collection (ProQuest)
- Anahtar Kelimeler: Cognitive mapping, DEMATEL, Fermatean fuzzy sets, Location selection, Solar energy, TOPSIS
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
Solar energy is gaining prominence as a renewable energy source due to its abundance and eco-friendliness. However, the recent increasing interest raises concerns about the sustainability of solar energy investments. Therefore, it is imperative to strategically choose an investment location problem, considering various criteria that encompass social, economic, and technical aspects. This study advocates an innovative and decision-centric approach, employing an integrated multi-stage Fermatean fuzzy-based multi-criteria decision-making methodology for the solution of this problem. The methodology systematically determines the importance weights of the criteria and prioritizes alternative sites to identify the optimal location for investments in solar power plant construction. In the initial stage, the focus lies on selecting the most suitable region for the installation of solar power plants. Subsequently, the second stage involves identifying the most appropriate location within the chosen region. Applied to the Türkiye case, the methodology designates the “Eastern Anatolia Region” as the most suitable region and identifies “Bingöl-Karlıova” as the most appropriate location. Sensitivity and comparative analyses validate the robustness of the decisions and verify the results. This paper significantly contributes to the existing literature by demonstrating the applied methodology, combining knowledge and experience from current market characteristics and engineering fields to improve the assessment process, providing human-centered design insights, and accelerating the development of renewable energy investments.