Assessing Computational Resources and Performance of Non-Intrusive Load Monitoring (NILM) Algorithms on Edge Computing Devices

Non-Intrusive Load Monitoring (NILM) enables appliance-level energy analysis from aggregated electrical signals, offering valuable insights for smart energy systems. While most NILM research focuses on high-resource environments, this study evaluates the feasibility of deploying NILM algorithms on constrained edge computing platforms. Two representative models for event detection and for energy disaggregation were trained on a high-end PC and tested on both the PC and two edge devices. A modular software framework using a virtual container and virtual environments ensured reproducibility across platforms. Experiments using datasets under simulated real-time streaming conditions revealed that although all devices achieved consistent detection, classification, and disaggregation performance, edge platforms struggled with real-time inference due to processing latency and memory limitations. This study presents a detailed comparison of execution time, resource usage, and model performance, highlighting the trade-offs associated with NILM deployment on embedded systems and proposing future directions for optimization and integration into smart grids.