Optimizing renewable energy investments using artificial intelligence-based multi-facet fuzzy decision models

Improving the performance of renewable energy projects is significant in the global energy transformation process. However, there is no consensus in the literature on which technical indicators are more determinant in these projects, making it difficult for investors and policy makers to make accurate and reliable decisions. To address this research gap, this study aims to optimize renewable energy investment strategies by identifying technical indicators as performance improvement criteria. The novelty of this study lies in the development of an integrated artificial intelligence–based decision-making framework that simultaneously incorporates parameter-driven artificial expert evaluations, dynamic multi-facet fuzzy sets, fuzzy cognitive maps, and principal component ranking optimization. Unlike existing studies, the proposed approach enables dynamic scenario-based adjustment of fuzzy membership parameters, allowing uncertainty to be modeled more realistically under negative, positive, unstable, and natural conditions. This integrated structure provides a more adaptive and data-driven prioritization of technical indicators compared to conventional fuzzy or multi-criteria models. The findings reveal that scalability and ease of maintenance are the most critical factors for enhancing technical performance in renewable energy projects. Accordingly, focusing on easy-to-service microgrids and maximizing lifecycle performance emerge as the most effective investment strategies.