What opportunities does the fusion of AI with UT present for sustainable energy solutions, including power-to-gas methane conversion and combined heat and power applications?

In the pursuit of sustainable energy solutions, the integration of Artificial Intelligence (AI) with Unscented Transformations (UT) presents a transformative approach to optimizing complex energy conversion processes. This study investigates the fusion of AI and UT within the context of power-to-gas methane conversion and combined heat and power (CHP) systems. By leveraging AI’s predictive learning capabilities and UT’s robust state estimation framework, the proposed hybrid approach enhances both accuracy and adaptability in dynamic energy environments. Simulation results reveal that the AI–UT fusion method achieves an average improvement of 12.14% in energy conversion efficiency across diverse methane conversion scenarios. Moreover, in CHP applications, the method delivers a 24.08% reduction in operational costs, primarily due to improved predictive maintenance, optimized load scheduling, and efficient resource allocation. These findings highlight the potential of AI–UT synergy to advance sustainable energy technologies by enabling intelligent, data-driven decision-making and real-time optimization. Overall, the proposed framework contributes to the development of more resilient, cost-effective, and environmentally responsible energy systems, marking a significant step toward achieving global sustainability objectives.