Tentative Program

This track focuses on the application of predictive analytics techniques to enhance the efficiency and reliability of smart grids. Researchers are invited to present their findings on forecasting energy consumption and load prediction using advanced data science methodologies.

This session explores the integration of machine learning algorithms in energy systems to optimize performance and decision-making. Contributions that demonstrate supervised and unsupervised learning applications for energy management are particularly welcome.

This track aims to showcase innovative deep learning approaches for analyzing and optimizing renewable energy sources. Papers that discuss feature extraction and anomaly detection in renewable energy data are encouraged.

This session highlights the role of Internet of Things (IoT) technologies in data analytics for smart energy management. Contributions that examine real-time monitoring and data-driven decision-making in IoT-enabled energy systems are sought.

This track is dedicated to exploring data-driven strategies for optimizing grid operations and management. Researchers are invited to present methodologies that enhance system reliability and efficiency through data analytics.

This session focuses on the development and application of anomaly detection techniques in energy consumption datasets. Papers that address challenges and solutions in identifying irregular patterns in energy usage are encouraged.