Artificial Intelligence-Based Expert Prioritizing and Hybrid Quantum Picture Fuzzy Rough Sets for Investment Decisions of Virtual Energy Market in the Metaverse

Improvements are necessary for the performance improvements of the digital twin technology developed for the virtual energy market on the Metaverse platform. However, more important factors need to be improved first to avoid excessive increases in costs. Thus, a priority analysis needs to be carried out to determine the variables that most affect the performance of technology investments. Accordingly, the purpose of this study is to evaluate the investments of digital twin technologies for virtual energy market in the Metaverse. A novel artificial intelligence-based fuzzy decision-making model is constructed to reach this objective. Firstly, the expert choices are prioritized with artificial intelligence-based decision-making method. Secondly, the investment priorities are analyzed for digital twin technologies with quantum picture fuzzy rough sets (QPFRS)-based Multi Stepwise Weight Assessment Ratio Analysis (M-SWARA). Finally, the alternatives for virtual energy market in the metaverse are ranked by VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje). There are limited studies in the literature that computes the weights of the experts while generating a decision-making model. Therefore, the main contribution of this study is integrating the artificial intelligence approach and fuzzy multi-criteria decision-making methodology. Within this scope, an artificial intelligence-based application is performed when creating the decision matrix. Owing to this issue, the importance weights of experts are determined according to the qualifications of these people. This situation contributes to the results obtained being more realistic. The findings demonstrate that operational performance is the most important indicator for the improvements of the digital twin technology investments for virtual energy markets in metaverse platform because it has the greatest weight (0.267). Furthermore, integrated data production is another critical factor for the performance increase of these projects with the weight of 0.257. It is also concluded that optimization of energy consumption with smart grids has the best ranking performance among the alternatives.