A machine learning and fuzzy logic model for optimizing digital transformation in renewable energy: Insights into industrial information integration

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration Pub Date : 2024-11-01 DOI:10.1016/j.jii.2024.100734

Serkan Eti , Serhat Yüksel , Hasan Dinçer , Dragan Pamucar , Muhammet Deveci , Gabriela Oana Olaru

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引用次数: 0

Abstract

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.

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优化可再生能源数字化转型的机器学习和模糊逻辑模型：对工业信息集成的启示

应确定改进可再生能源项目数字化转型的最基本标准。这种情况有助于企业以最有效的方式利用有限的财务预算和人力资源。因此，需要开展一项新的研究，分析可再生能源项目数字化转型过程中的绩效指标。因此，本研究旨在为这些项目确定最重要的数字化转型绩效指标。在此过程中，构建了一个基于机器学习和模糊逻辑的三阶段决策模型。第一阶段包括通过降维方法计算专家权重。其次，通过标准间相关性（CRITIC）的费马特模糊标准重要性对可再生能源项目数字化转型的基本因素进行研究。最后一部分是通过费马特式模糊加权汇总产品评估（WASPAS）对新兴七国进行排名。另一方面，在这一过程中还考虑了综合折中方案（CoCoSo）方法，以进行比较评价。本研究的主要贡献在于生成了新颖的机器学习和模糊逻辑综合决策模型，以进行与可再生能源项目数字化转型相关的评估。在该模型中，机器学习技术用于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，通过最大限度地减少不确定性和确定项目之间的关系，也为文献做出了贡献。研究结果表明，在可再生能源项目的数字化转型过程中，聘用合格人才发挥着最关键的作用。此外，政府的支持对于可再生能源项目成功实施数字化转型过程也非常关键。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Industrial Information Integration Decision Sciences-Information Systems and Management

CiteScore

22.30

自引率

13.40%

发文量

100

期刊介绍： The Journal of Industrial Information Integration focuses on the industry's transition towards industrial integration and informatization, covering not only hardware and software but also information integration. It serves as a platform for promoting advances in industrial information integration, addressing challenges, issues, and solutions in an interdisciplinary forum for researchers, practitioners, and policy makers. The Journal of Industrial Information Integration welcomes papers on foundational, technical, and practical aspects of industrial information integration, emphasizing the complex and cross-disciplinary topics that arise in industrial integration. Techniques from mathematical science, computer science, computer engineering, electrical and electronic engineering, manufacturing engineering, and engineering management are crucial in this context.