程龙教授在中科院一区TOP期刊Information Fusion (IF：14.7) 牵头主办专题期刊“Data Fusion in Modern Energy Systems”，聚焦于能源系统中数据融合技术的最新进展与应用，旨在基于最新的计算机和AI技术推动智能能源管理与优化领域的研究与实践。
Data Fusion in Modern Energy Systems
Modern energy systems encompass a wide range of technologies and infrastructures, including renewable energy sources like solar and wind power, advanced energy storage solutions such as batteries and pumped hydro storage, and smart grids. The integration and coordination of these diverse components present significant challenges in data processing for effective management and operation due to their complexity and variability. Leveraging multi-modal data and employing multi-model approaches, data fusion can merge information from various sensors, measurement devices, and data streams. This enables comprehensive monitoring, improved fault detection, enhanced predictive maintenance, and optimized control strategies for modern energy systems. However, applying the appropriate data fusion strategy to design an advanced energy system is not straightforward. This complexity arises from the need to carefully consider the specific characteristics of the energy system, the types of data involved, and the desired outcomes, making the process highly intricate and context-dependent.
This special issue focuses on data fusion technologies in modern energy systems, aiming to provide the research community with a deeper understanding of data fusion strategies for building advanced energy systems, along with their principles, advantages, and potential applications. By addressing the challenges in the integration and management of these systems, it seeks to demonstrate how cutting-edge data fusion methods can transform the efficiency, reliability, and sustainability of modern energy systems.
Topics of interest include, but are not limited to:
- Innovative data fusion techniques for renewable energy integration
- Real-time data fusion for smart grid management
- Predictive maintenance in energy systems using data fusion
- Multi-modal data fusion for enhanced energy storage management
- Data fusion methods for cyber-physical systems in energy
- Data fusion for fault detection and diagnosis in power systems
- Optimization of energy distribution networks through data fusion
- Data fusion in microgrid management and operation
- Environmental impact assessment using data fusion in energy systems
Guest editors:
Long Cheng, PhD
North China Electric Power University, Beijing, China
Shan Zuo, PhD
University of Connecticut, Storrs, CT, USA
Pedro P. Vergara, PhD
Delft University of Technology, Delft, Netherlands
Tomas Ward, PhD
Dublin City University, Dublin, Ireland
Xin Ning, PhD
Chinese Academy of Sciences, Beijing, China
