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<p>The rapid evolution of green energy management systems is transforming how organizations plan, monitor and optimize sustainable energy usage. However, selecting the most efficient strategies and technologies for renewable energy integration remains challenging due to uncertain operational data, fluctuating energy demands and incomplete expert evaluations. This study leverages the cubical fuzzy set (CFS) framework to capture the positive, neutral and negative degrees of decision attributes, providing a comprehensive representation of uncertainty in renewable energy decision-making. Two innovative aggregation operators (AOs) are proposed: the Cubical Fuzzy Interactive Dubois–Prade Weighted Average Aggregation Operator (CFIDPWAAO) and the Cubical Fuzzy Interactive Dubois–Prade Ordered Weighted Average Aggregation Operator (CFIDPOWAAO). A numerical example for selecting green energy technologies–including solar, wind and hybrid energy solutions–is provided using a cubical fuzzy decision matrix. Comparative and sensitivity analyses demonstrate that the proposed operators achieve more consistent rankings across varying parameter values, with stability rates exceeding 95% in sensitivity tests and improved agreement with expert evaluations compared to existing operators. These results quantitatively confirm the reliability, robustness and flexibility of the proposed framework for data-driven sustainable energy decision-making.</p>

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A cubical fuzzy Dubois–Prade aggregation framework for renewable and sustainable green energy decision-making

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • K. Deva,
  • S. Sangeetha,
  • Fikadu Tesgera Tolasa,
  • Jiregna Abdissa Olana,
  • Segni Adugna Tamune

摘要

The rapid evolution of green energy management systems is transforming how organizations plan, monitor and optimize sustainable energy usage. However, selecting the most efficient strategies and technologies for renewable energy integration remains challenging due to uncertain operational data, fluctuating energy demands and incomplete expert evaluations. This study leverages the cubical fuzzy set (CFS) framework to capture the positive, neutral and negative degrees of decision attributes, providing a comprehensive representation of uncertainty in renewable energy decision-making. Two innovative aggregation operators (AOs) are proposed: the Cubical Fuzzy Interactive Dubois–Prade Weighted Average Aggregation Operator (CFIDPWAAO) and the Cubical Fuzzy Interactive Dubois–Prade Ordered Weighted Average Aggregation Operator (CFIDPOWAAO). A numerical example for selecting green energy technologies–including solar, wind and hybrid energy solutions–is provided using a cubical fuzzy decision matrix. Comparative and sensitivity analyses demonstrate that the proposed operators achieve more consistent rankings across varying parameter values, with stability rates exceeding 95% in sensitivity tests and improved agreement with expert evaluations compared to existing operators. These results quantitatively confirm the reliability, robustness and flexibility of the proposed framework for data-driven sustainable energy decision-making.