★ Abstract of the track

Artificial Intelligence (AI) is revolutionizing the fields of renewable energy and electrical electronic engineering by enabling smarter, more efficient solutions to complex scientific challenges. In renewable energy, AI algorithms optimize the performance of solar panels, wind turbines, and energy storage systems by predicting weather patterns, managing energy distribution, and enhancing system reliability. It leads to increased energy efficiency and reduces operational costs, accelerating the transition to sustainable power sources. In electrical and electronic engineering, AI facilitates the design and control of advanced circuits and systems. Machine learning models analyze vast datasets to improve fault detection, predictive maintenance, and system optimization, ensuring higher reliability and longer lifespans for electronic devices. AI-driven automation also streamlines manufacturing processes, reducing errors and boosting productivity.

★ Background

AI supports the integration of renewable energy into smart grids, balancing supply and demand dynamically to maintain grid stability. By combining data from sensors, meters, and weather forecasts, AI systems enable real-time decision-making that enhances energy management and reduces carbon footprints. Overall, AI acts as a powerful tool in advancing scientific research and practical applications within renewable energy, as well as the electrical and electronic engineering, fostering innovation and sustainability for a cleaner, more efficient future.

Subject & Research Domains

Subject: AI for Sparse Data and High-Dimensional Engineering Problems

• AI in Electrical & Electronic Engineering
• Machine Learning
• AI in life science

Research Domains:

AI Techniques Relevant to Renewable Energy and Electrical & Electronic Engineering:

• Machine Learning (ML) and Deep Learning (DL)
• Data Analytics and Predictive Modeling
• Reinforcement Learning

AI in Electrical & Electronic Engineering:

• Data quality and availability
• Computational complexity and resource requirements

Topics

• Machine Learning and AI algorithms for electrical electronic engineering
• AI-Enabled Real-Time Monitoring and Control
• AI-Based Control Systems
• Integration of AI in Energy Storage Systems for Renewable Energy Applications
• Application for Reinforcement Learning

Recommended Invited Speaker

David Chieng
Associate Professor in Electrical & Electronic Engineering, University of Nottingham Ningbo China