This track focuses on the latest methodologies and technologies in energy analytics, emphasizing their application in optimizing energy consumption and enhancing operational efficiency. Researchers are invited to present innovative approaches that leverage data analytics for improved energy management.

Exploring the intersection of smart grid technologies and data analytics, this track aims to discuss how integrated data systems can enhance grid reliability and efficiency. Contributions should address challenges and solutions in data integration for smart grid applications.

This track invites papers that explore predictive modeling techniques for forecasting energy demand and supply. Emphasis will be placed on the use of machine learning and statistical methods to enhance decision-making in energy systems.

Focusing on the role of big data in the renewable energy sector, this track seeks to highlight innovative applications that drive sustainability and efficiency. Researchers are encouraged to share insights on data-driven strategies for renewable energy deployment.

This track examines the application of machine learning algorithms in energy management systems, focusing on their effectiveness in optimizing resource allocation and consumption. Papers should present empirical studies or theoretical advancements in this domain.

This track is dedicated to the development and implementation of decision support systems that utilize data analytics for energy-related decision-making. Contributions should demonstrate how these systems can enhance strategic planning and operational efficiency.