Towards Real-world Deployment of NILM Systems: Challenges and Practices
This addresses deployment challenges for NILM systems in real-world energy monitoring, though it is incremental as it builds on existing NILM methods with a new deployment approach.
The paper tackles the high computation costs and service delays in cloud-exclusive non-intrusive load monitoring (NILM) systems by proposing a three-tier edge-cloud collaboration framework, achieving high decomposition accuracy while significantly reducing cloud workload and communication overhead.
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.