Real-time monitoring and optimization methods for user-side energy management based on edge computing

Area of Science:

• Energy Systems Engineering
• Computer Engineering
• Artificial Intelligence

Background:

• Traditional centralized energy management systems face challenges in real-time data processing and optimization.
• Decentralized approaches are needed to address the increasing complexity of user-side energy management.

Purpose of the Study:

• To propose a comprehensive framework for real-time monitoring and optimization of user-side energy management systems using edge computing.
• To address limitations of centralized systems by moving computation closer to end devices.

Main Methods:

• Developed an edge computing-based system architecture for data acquisition and processing.
• Implemented real-time monitoring for energy consumption and power quality.
• Applied optimization techniques for demand response and distributed energy resource coordination.

Main Results:

• Demonstrated significant improvements in energy efficiency, response time, and cost reduction compared to centralized approaches.
• Achieved up to a 30% increase in renewable energy utilization.
• Reported a 25% reduction in operating costs across various deployment scenarios.

Conclusions:

• The proposed edge computing framework offers a viable solution for next-generation energy management systems.
• Edge computing enables more efficient and cost-effective energy management.
• Further research is needed to address remaining challenges and explore future directions.