Design and optimization of distributed energy management system based on edge computing and machine learning

Design and optimization of distributed energy management system based on edge computing and machine learning

연구 분야: Networking

학회: Energy Informatics

With the continuous growth of global energy demand and the rapid development of renewable energy, traditional energy management systems are facing enormous challenges, especially in the scheduling and optimization of distributed energy. In order to meet these challenges, edge computing and machine learning technology are widely used in the design and optimization of distributed energy management systems. This paper proposes a design scheme of distributed energy management system based on edge computing and machine learning, and optimizes it. The system reduces data transmission latency and improves energy scheduling efficiency by performing real-time data processing and analysis on edge devices. The experimental results show that the proposed system performs outstandingly in optimizing energy allocation, reducing energy consumption, and improving system response speed. Specifically, by using machine learning algorithms for dynamic scheduling of distributed energy resources, the system can achieve an energy utilization rate 12% higher than traditional scheduling methods, and reduce energy waste by 18% in the event of fluctuations in energy demand. In addition, the system response time has been improved by 30% compared to traditional cloud-based solutions. These optimizations not only reduce energy costs, but also effectively enhance the sustainability and intelligence level of distributed energy systems. The contribution of this research lies in the combination of edge computing and machine learning technology to achieve real-time optimal control of the distributed energy system, reduce the system’s computing load and delay, and improve the accuracy and flexibility of energy management through data-driven methods. Future research can further explore how to integrate multiple machine learning algorithms to optimize energy scheduling strategies and improve the system’s adaptability in complex environments.