用数字孪生+AI物联网，让微电网更可靠、更省钱。
AI-Enhanced IoT Systems for Predictive Maintenance and Affordability Optimization in Smart Microgrids: A Digital Twin Approach
- 通过数字孪生同步物理与虚拟微电网，实时监控状态
- 故障预测准确率提升，运维停机时间减少，成本降低
- 适合智能电网、能源管理研究者和工程师参考
本研究提出一种基于数字孪生的AI增强型物联网框架，用于智能微电网的预测性维护与经济性优化。系统整合实时传感器数据、基于机器学习的故障预测及成本敏感型运营分析，提升分布式微电网的可靠性与能效。通过将物理微电网组件与虚拟数字孪生体同步，实现部件退化早期检测、动态负荷管理与优化维护调度。实验表明，相比传统方法，该框架在预测准确性、运行停机时间与成本节约方面均有显著改善。结果表明，数字孪生驱动的物联网架构是下一代智能且经济型能源系统的可扩展解决方案。
原文摘要 · Abstract (English)
This study presents an AI enhanced IoT framework for predictive maintenance and affordability optimization in smart microgrids using a Digital Twin modeling approach. The proposed system integrates real time sensor data, machine learning based fault prediction, and cost aware operational analytics to improve reliability and energy efficiency in distributed microgrid environments. By synchronizing physical microgrid components with a virtual Digital Twin, the framework enables early detection of component degradation, dynamic load management, and optimized maintenance scheduling. Experimental evaluations demonstrate improved predictive accuracy, reduced operational downtime, and measurable cost savings compared to baseline microgrid management methods. The findings highlight the potential of Digital Twin driven IoT architectures as a scalable solution for next generation intelligent and affordable energy systems.
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