Addressing the missing gap in uninterrupted solar energy research through an automated literature mining and fuzzy decision support system

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This study addresses the uncertainty problem in determining the most effective strategies for uninterrupted solar energy production. A significant gap in the literature is the limited number of comprehensive, data-driven analyses regarding which criteria should be prioritized in solar energy projects, and which next-generation technologies offer more suitable alternatives. Therefore, the aim of this study is to develop an integrated framework combining automated literature mining, objective statistical weighting, and fuzzy multi criteria decision making that enables the objective determination of both criteria and strategy alternatives. Within the model, six key criteria and six strategy alternatives were identified by analyzing the summaries of 724 studies on the Web of Science platform using automated data acquisition techniques. Criterion weights were calculated using the COWEB approach developed by the authors, while expert weights were determined using machine learning. Furthermore, Sierpinski fuzzy sets were integrated into the model to more accurately represent uncertainties in the decision structure. According to the findings, energy storage compatibility and grid integration (0.221) and production, installation, and maintenance costs (0.213) emerged as the most influential evaluation criteria. Among the identified technological pathways, bifacial solar panels achieved the highest priority score (0.137), followed by TOPCON technology (0.120). These findings indicate that uninterrupted solar energy production depends not only on advances in photovoltaic efficiency but also on the effective integration of storage infrastructure and cost management practices. The results provide practical guidance for policymakers, investors, and energy companies seeking to improve the reliability and long-term sustainability of solar energy systems.