An integrated intelligent decision support framework for the development of photovoltaic solar power

Bouraima M. B., Ayyıldız E., Badi İ., Özçelik G., Yeni F. B., Pamucar D.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, cilt.127, 2024 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 127
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.engappai.2023.107253
  • Dergi Adı: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

An emerging question for photovoltaic (PV) solar power development is how to ascertain the optimal choice from a finite set of available alternatives under numerous conflicting criteria as well as high levels of imprecise, vague, and uncertain information. For the first time, we investigate the prioritization of the alternatives for the development of PV solar power via the interval-valued intuitionistic fuzzy sets (IVIFSs), which show great power in capturing ambiguous, uncertain, and vague information, and mitigating information loss. This paper presents a novel integrated intelligent decision support system comprising of Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, Analytic Hierarchy Process (AHP), and Combined Compromise Solution (CoCoSo) within an interval-valued intuitionistic fuzzy (IVIF) framework. Four alternatives are considered. To rank these alternatives, twelve criteria are defined under four aspects of SWOT analysis based on literature review and discussion with decision-makers. Subsequently, the IVIF-AHP method is utilized to determine the weights assigned to each criterion and sub-criterion. Finally, the IVIF-CoCoSo method is employed to rank four alternatives. The results showed that giving proactive attention to mitigating potential adverse environmental impacts of PV solar systems is the most highly prioritized strategy for PV solar power development. The results of the comparative and sensitivity analyses showed that the proposed method generates highly robust outcomes. The formulated integrated intelligent decision support system can help energy policy authorities with a valuable resource to craft optimal techniques for developing PV solar power.