Application of Edge Artificial Intelligence in Energy Systems

The integration of artificial intelligence (AI) and edge computing is revolutionizing the conventional power industry by enhancing efficiency, reliability, and sustainability. Edge AI leverages the transformative potential of machine learning within the performance-constrained realm of embedded systems, which provides the interface between Internet of Things (IoT) technology and modern power systems. Highly responsive AI at the edge considerably outperforms the centralized IoT model typically deployed in smart grids. Furthermore, edge AI offers enhanced levels of safety and security in smart grids. Data transmitted via internet-connected devices are susceptible to breaches and cybercrimes. The cloud-edge orchestrated style potentially supports advanced applications in modern power systems, such as dynamically balancing supply and demand, grid data security, and predictive maintenance.

The primary goal of this topic is to explore how AI and edge computing can synergistically improve energy systems. Specifically, it aims to illustrate the benefits of combining these technologies to achieve real-time monitoring and control, enhance predictive capabilities, and optimize energy distribution. By providing case studies and practical examples, the goal is to highlight how AI-driven analysis and edge-based processing models can address current challenges in energy management, such as energy forecasting, grid stability, power dispatch, and predictive maintenance.

The Research Topic seeks contributions that explore the application of AI and edge computing within energy systems. Topics of interest include, but are not limited to:
- Energy forecasting
- Real-time data processing
- Strategies for predictive maintenance
- Optimization techniques for energy consumption

Authors are encouraged to submit research findings, case studies, and reviews that offer innovative insights or practical implementations of these technologies. Submissions should be clear and well-organized and contribute to advancing the understanding of how AI and edge computing can enhance energy system performance. Manuscripts will undergo peer review for relevance, originality, and technical quality.