The most essential criteria to improve digital transformation in renewable energy projects should be identified. This situation helps the companies to use limited financial budgets and human resources in the most efficient way. Therefore, a new study is needed to analyze the performance indicators of the digital transformation process in renewable energy projects. Accordingly, this study aims to identify the most significant performance indicators of digital transformation for these projects. A three-stage machine learning and fuzzy logic-based decision-making model has been constructed in this process. The first stage includes the weight calculation of the experts by dimension reduction methodology. Secondly, essential factors of digital transformation in renewable energy projects are examined via Fermatean fuzzy criteria importance through intercriteria correlation (CRITIC). The final part consists of the ranking of emerging seven countries with Fermatean fuzzy weighted aggregated sum product assessment (WASPAS). On the other side, combined compromise solution (CoCoSo) method is also taken into consideration in this process to make a comparative evaluation. The main contribution of this study is the generation of novel machine learning and fuzzy logic integrated decision-making model to make evaluation related to the digital transformation of renewable energy projects. In this model, machine learning technique is used to determine the importance weights of the experts. Similarly, integrating Fermatean fuzzy numbers with CRITIC and WASPAS techniques also contributes to the literature by minimizing the uncertainty and identifying the relationship between the items. The findings demonstrate that employing qualified personnel plays the most critical role in increasing digital transformation in renewable energy projects. Additionally, government support is very critical in the successful implementation of digital transformation processes in renewable energy projects.

中文翻译：

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用于优化可再生能源数字化转型的机器学习和模糊逻辑模型：工业信息集成洞察


应确定改善可再生能源项目数字化转型的最基本标准。这种情况有助于公司以最有效的方式利用有限的财务预算和人力资源。因此，需要一项新的研究来分析可再生能源项目中数字化转型过程的绩效指标。因此，本研究旨在确定这些项目数字化转型最重要的绩效指标。在此过程中构建了一个三阶段机器学习和基于模糊逻辑的决策模型。第一阶段包括通过降维方法计算 EA 的权重。其次，通过标准间相关性 （CRITIC） 的 Fermatean 模糊标准重要性检查可再生能源项目中数字化转型的基本因素。最后一部分包括采用 Fermatean 模糊加权聚合和产品评估 （WASPAS） 的新兴 7 个国家的排名。另一方面，在此过程中还考虑了组合折衷解决方案 （CoCoSo） 方法以进行比较评估。本研究的主要贡献是生成新颖的机器学习和模糊逻辑集成决策模型，以做出与可再生能源项目数字化转型相关的评估。在此模型中，使用机器学习技术来确定专家的重要性权重。同样，将 Fermatean 模糊数与 CRITIC 和 WASPAS 技术相结合也有助于最大限度地减少不确定性并确定项目之间的关系。 研究结果表明，雇用合格人员在促进可再生能源项目的数字化转型方面发挥着最关键的作用。此外，政府的支持对于在可再生能源项目中成功实施数字化转型流程至关重要。