LARGE EXPERT DATA-BASED Q-LEARNING ASSESSMENT WITH HYBRID MOLECULAR FUZZY MODELLING FOR PEER-TO-PEER ENERGY TRADING USING BLOCKCHAIN TECHNOLOGIES

Abstract Peer-to-peer (P2P) energy trading is an industry where energy producers and consumers buy and sell energy directly by using a platform and in most cases, the technology behind P2P energy trading is blockchain. Tracking performance indicators of blockchain-based P2P energy trading is important to evaluate the effectiveness of a project and to help investors manage resources effectively. However, the literature is still scarce, and the risks of strategic decision making are higher. To fill this gap, we present a novel method to prioritize strategies in P2P energy trading. The model combines molecular fuzzy-based cognitive maps with molecular fuzzy ranking. It contributes to the literature by creating a novel method for ranking alternatives across different geometric shapes. So, the testing of reliability of ranking can be done and the accuracy and robustness of the model can be improved. The Q-learning approach also allows expert weights to be calculated objectively which mitigates the subjectivity of the results and helps investors make informed decisions. By providing a reliable framework for strategy development, this study contributes significantly to the literature and thus investors can make well-informed decisions. The results show that blockchain scalability and grid integration are the most critical performance indicators to enhance these projects. Community empowerment through local partnerships for microgrid development is also the most important investment option.