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.
                   
													 
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													                    Olusola Bamisile, Chengdu University of Technology, China | 
									
Biography: 
										 TBA...