提出边缘云协同的三层次框架，提升非侵入式用电监测系统落地能力。
Towards Real-world Deployment of NILM Systems: Challenges and Practices
- 边缘部署轻量模型，云端用深度学习模型，分工协作降低资源消耗。
- 实测显示分解准确率高，云端负载和通信开销显著减少。
- 针对实际部署设计专用方案，打通算法到应用的落地鸿沟。
非侵入式负载监测（NILM）作为关键负载监控技术，可大幅降低传统电力传感器的部署成本。以往研究多集中于仅限云端的NILM算法，常导致计算成本高、服务延迟大。为解决此问题，本文提出一种三层框架，通过边缘-云协同提升NILM系统的实际可用性。结合边缘与云端的计算资源，分别在边缘侧实现轻量级NILM模型，在云端部署基于深度学习的模型。除差异化模型设计外，还定制了专用于NILM的部署方案，集成Gunicorn与NGINX，弥合理论算法与实际应用间的差距。通过真实场景下的数据采集、模型训练与系统部署全流程验证，结果表明：该框架在实际条件下实现了高分解精度，同时显著降低云端负载与通信开销。
原文摘要 · Abstract (English)
Non-intrusive load monitoring (NILM), as a key load monitoring technology, can much reduce the deployment cost of traditional power sensors. Previous research has largely focused on developing cloud-exclusive NILM algorithms, which often result in high computation costs and significant service delays. To address these issues, we propose a three-tier framework to enhance the real-world applicability of NILM systems through edge-cloud collaboration. Considering the computational resources available at both the edge and cloud, we implement a lightweight NILM model at the edge and a deep learning based model at the cloud, respectively. In addition to the differential model implementations, we also design a NILM-specific deployment scheme that integrates Gunicorn and NGINX to bridge the gap between theoretical algorithms and practical applications. To verify the effectiveness of the proposed framework, we apply real-world NILM scenario settings and implement the entire process of data acquisition, model training, and system deployment. The results demonstrate that our framework can achieve high decomposition accuracy while significantly reducing the cloud workload and communication overhead under practical considerations.
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