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

David Serna (), Carlos Arias, Tatiana Manrique, Alejandro Guerrero () and Javier Sierra
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David Serna: EnergEIA Research Group, Universidad EIA, km 2 + 200 Vía al Aeropuerto JMC, Envigado 055428, Colombia
Carlos Arias: Department of Electronic Engineering, Universidad de Sucre, Sincelejo 700001, Colombia
Tatiana Manrique: Department of Mechatronics and Electromechanics, Institución Universitaria ITM, Medellin 050034, Colombia
Alejandro Guerrero: Department of Electronic Engineering, Universidad de Sucre, Sincelejo 700001, Colombia
Javier Sierra: Department of Electronic Engineering, Universidad de Sucre, Sincelejo 700001, Colombia

Energies, 2025, vol. 18, issue 22, 1-28

Abstract: 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.

Keywords: edge computing; energy disaggregation; energy efficiency; machine learning; non-intrusive load monitoring; real-time inference; smart energy monitoring (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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