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...