Optimizing decision-making for sustainable solar thermal energy systems within an intuitionistic fuzzy environment

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.