Akademik Məlumat İdarəetmə Sistemi
Engineering Applications of Artificial Intelligence, vol.167, 2026 (SCI-Expanded, Scopus)
As the global energy system increasingly transforms to renewable energy sources (RERs), such as solar, wind, hydro, biomass, and geothermal, there is an increasing need for intelligent and robust decision-making models that can assess sustainability in multiple dimensions, including environmental, economic, technical, and social. However, existing multi-criteria decision-making (MCDM) methods often struggle with modeling uncertainty and subjectivity. They typically lack the flexibility to integrate both objective data and expert judgment in a unified, interpretable framework. To address these limitations, this paper proposes an artificial intelligence-driven hybrid decision support system combined with linear Diophantine fuzzy sets (LDFSs) to model the uncertainty in RERs evaluation. We first introduce new entropy and divergence measures for LDFSs to accurately quantify uncertainty and information diversity, overcoming the shortcomings of existing measures that may yield inconsistent outcomes. Subsequently, we integrate entropy-divergence measures (EDM) for objective criteria weighting with step-wise weight assessment ratio analysis (SWARA) to incorporate subjective expert preferences, ensuring a balanced weighting strategy. Finally, we employ the operational competitiveness rating analysis (OCRA) method to rank alternatives, enhancing its interpretability in complex decision-making. The proposed model is validated through a case study on the evaluation of RERs. The model successfully ranked alternatives based on sustainability criteria, identifying wind energy as the optimal choice for sustainable energy deployment. Sensitivity and comparison analysis further confirm the robustness and effectiveness of the proposed model. This work provides a scientifically rigorous and practical artificial intelligence-based tool to support informed decision-making for sustainable RERs deployment in diverse and uncertain contexts.