This track focuses on the integration of artificial intelligence technologies in renewable energy systems. It aims to explore innovative applications and methodologies that enhance the efficiency and effectiveness of renewable energy sources.

This session will delve into the use of artificial neural networks in various energy applications, including solar thermal and photovoltaic systems. Participants will discuss advancements in modeling and prediction capabilities facilitated by neural network architectures.

This track emphasizes the role of genetic algorithms and other optimization techniques in improving renewable energy systems. It will cover case studies and theoretical advancements that demonstrate enhanced performance and efficiency.

This session will explore the application of predictive control algorithms for energy systems, including wind speed prediction and power generation. Attendees will examine the effectiveness of these control strategies in real-world scenarios.

This track investigates the application of fuzzy logic systems in the control and modeling of energy management systems. Discussions will focus on the benefits of fuzzy approaches in handling uncertainty and complexity in energy applications.

This session will highlight the use of computational intelligence techniques in architectural and building acoustics. Participants will share insights on how these techniques can optimize energy consumption and enhance building performance.