AI-Enhanced IoT Energy Management: Integrating Monte Carlo Simulation, Game-Theoretic Approaches, and Agent-based Modeling for Sustainable Solutions

More advanced solutions are required due to the growing complexity of energy markets and supply chain management. Novel approaches like agent-based modelling, game theory, and Monte Carlo simulation offer scalable, efficient, and adaptive solutions to solve sustainability issues and improve operations. This paradigm enhances energy management, process optimization, and data-based decision-making by fusing agent-based modelling to support decentralized system evaluation, Monte Carlo simulation for supporting uncertainty handling, and game theory for allocation of resources. Although ensuring sustainability, flexibility, and seamless integration into the system, it strives to improve energy prediction, resource planning, and abnormality identification. It outperforms traditional methods with 94% accuracy, 95% F1 score, and 93% scalability, setting a new benchmark for supply chain and sustainable energy management.