International Conference on Energy Systems and Data Analytics (ICESDA - 26)

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.

Exploring the role of analytics in promoting sustainability within energy systems, this track invites discussions on metrics, frameworks, and tools that assess environmental impact. Papers should highlight innovative approaches to integrating sustainability into energy analytics.

This track focuses on the integration of Internet of Things (IoT) technologies in energy systems and their impact on data optimization. Researchers are encouraged to explore how IoT can enhance data collection, analysis, and overall energy efficiency.

This track aims to showcase innovative data visualization techniques that facilitate the interpretation and communication of energy analytics findings. Contributions should demonstrate how effective visualization can enhance stakeholder engagement and decision-making.

Focusing on the methodologies for risk assessment in energy systems, this track invites papers that address the identification and mitigation of risks through data analytics. Emphasis will be placed on quantitative and qualitative approaches to risk management.

This track examines the role of cloud computing in enhancing energy analytics platforms, focusing on scalability, accessibility, and data management. Researchers are invited to discuss the implications of cloud integration for real-time energy data analysis.