Title : AI-driven energy conversion and storage: From electrocatalytic processes to intelligent renewable energy systems
Abstract:
The global transition towards sustainable energy requires the integration of renewable generation, advanced energy conversion, energy storage, and intelligent power management. This keynote explores how Artificial Intelligence (AI), machine learning, and data-driven intelligence can accelerate the transformation of emerging energy technologies into efficient, reliable, and sustainable energy systems. Particular emphasis will be placed on the convergence of electrocatalytic energy conversion, renewable energy technologies, battery and hybrid energy storage, power electronic converters, and intelligent grid integration. The talk will highlight the role of AI-based prediction, optimisation, digitalisation, and intelligent control in improving energy-conversion efficiency, optimising storage utilisation, forecasting renewable generation, and enabling adaptive energy management. The keynote will further discuss emerging opportunities at the intersection of green hydrogen, electrochemical energy systems, renewable-integrated microgrids, next-generation energy storage, and AI-enabled energy management. Key challenges related to data availability, model interpretability, cybersecurity, scalability, and real-time implementation will also be addressed. Finally, the presentation will propose a vision for AI-enabled, resilient, and sustainable energy ecosystems, emphasising the need for interdisciplinary collaboration among catalysis, electrochemical technologies, materials science, power electronics, renewable energy, and artificial intelligence to accelerate the transition from fundamental technological advances to practical, scalable sustainable energy solutions.
