International Conference on Applications of Artificial Intelligence in Energy Systems (ICAAIES-27)

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

This session will delve into the utilization of artificial neural networks in various energy applications. Participants will discuss methodologies and case studies that demonstrate the effectiveness of neural networks in optimizing energy systems.

This track emphasizes the role of genetic algorithms and other optimization techniques in solar thermal systems. Presentations will cover advancements in optimizing system performance and energy output.

This session will explore the application of control algorithms for predicting wind speed and active power generation. The focus will be on enhancing the reliability of energy generation through predictive modeling.

This track addresses the application of fuzzy logic systems in the control and modeling of building energy systems. Discussions will highlight the benefits of fuzzy systems in improving energy efficiency and occupant comfort.

This session will investigate the application of computational intelligence techniques in architectural and building acoustics. The aim is to enhance the design and functionality of spaces through intelligent acoustic modeling.

This track will focus on the application of artificial intelligence techniques in combustion systems. Participants will share insights on how AI can optimize combustion processes and reduce emissions.

This session will explore innovative artificial intelligence techniques aimed at reducing energy consumption across various sectors. Case studies will illustrate successful implementations and their impact on sustainability.

This track will cover advancements in fault diagnostics and maintenance strategies for energy systems using artificial intelligence. The focus will be on predictive maintenance and its role in enhancing system reliability.

This session will discuss active learning strategies for the neural estimation of engine maps. The aim is to improve the accuracy of performance predictions and enhance engine efficiency.

This track will focus on the latest developments in process monitoring, control, scheduling, and planning within energy systems. Participants will explore how AI can streamline operations and improve overall system performance.