The global transition to net-zero emissions requires intelligent solutions to optimize renewable energy systems, enhance grid stability, and accelerate decarbonisation. This special session solicits original research contributions that focuses on the application of Artificial Intelligence (AI) and machine learning (ML) techniques in advancing renewable energy integration, improving energy efficiency, and sustainable energy transitions.
We invite high-quality research contributions on AI-driven innovations, including but not limited to:
AI for Renewable Energy Forecasting & Optimization
• Solar PV and wind
power prediction using deep learning,
machine learning, and hybrid models
• Short-term and long-term load
forecasting for smart grids and
microgrids
• Energy consumption prediction in
buildings with AI-based models, digital
twins, and IoT analytics
• Probabilistic forecasting for
uncertainty-aware renewable energy
scheduling
AI for Smart Grids & Decarbonisation
• AI-powered demand
response and energy management
• Optimal energy storage control using
reinforcement learning and multi-agent
systems
• Carbon footprint tracking and
reduction with AI-driven lifecycle
assessment
AI for Fault Detection & System Reliability
• Deep learning-based
anomaly detection in solar PV plants and
wind farms
• Predictive maintenance for renewable
energy systems using explainable AI
• Real-time fault diagnosis in hybrid
renewable-storage grids
• Resilience enhancement against
cyber-physical threats in smart grids
This session aims to foster interdisciplinary dialogue between AI researchers, energy engineers, and policymakers to accelerate the transition to sustainable energy systems. You are welcome to submit theoretical, methodological, and applied research with rigorous validation through case studies, or real-world deployments. Submissions should demonstrate scalability, interpretability, and impact for a sustainable energy transition.
![]() |
Olusola Bamisile, Chengdu University of Technology, China |
Biography:
TBA...