Optimizing decision-making for sustainable solar thermal energy systems within an intuitionistic fuzzy environment
Year: 2025
Type: article
Abstract: In the context of addressing global energy needs and climate change, the transition to sustainable energy systems has become one of the most important objectives, and in this context. Solar-thermal energy systems are among the most important sources of renewable energy solutions. However, existing research on solar-thermal energy systems often falls short in effectively handling uncertainty and the credibility of expert evaluations, which are essential for ensuring reliable decision-making. Traditional methods still focus on deterministic or simplified models, neglecting the intricate and uncertain evaluations present in real-world situations. To address these shortcomings, this study employs intuitionistic fuzzy credibility numbers (IFCNs), which explicitly incorporate membership, non-membership, and credibility degrees, enabling a more comprehensive representation of uncertain and hesitant information. Based on IFCNs, we introduce a new, more effective method of handling credibility in aggregation named the generalized intuitionistic fuzzy credibility numbers weighted averaging (GIFCNWA) operator. Based on this operator, a multi-attribute decision-making algorithm is proposed that integrates attribute importance and reliability of evaluations. The proposed methodology is applied to a real-world decision-making problem in solar thermal energy storage by considering four options: nanostructured absorber coatings, advanced phase-change materials, high-temperature alloys, and adaptive control systems. The results identify nanostructured absorber coatings as the most optimal solution. A parametric sensitivity analysis is conducted, demonstrating the robustness and stability of the proposed operator under varying parameter conditions. A comparative analysis with existing decision-making techniques reveals the superiority and consistency of the proposed operator in delivering reliable results.
Authors: Muhammad Zafar Abbas, Rukhshanda Anjum
Institution: University of Lahore
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4416066244