Advanced Feature Engineering for Non-Intrusive Load Monitoring
Non-Intrusive Load Monitoring (NILM) aims to identify electrical appliances in a household from voltage and current signals, enabling energy efficiency and smart grid applications. This study investigates NILM classification using the PLAID sub-metered dataset, evaluating multiple feature extraction...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:dnet:idus________::02d40ccb6e39726d2e9ea39c041b28a7 |
| Acceso en línea: | https://hdl.handle.net/11441/184104 https://doi.org/10.1109/ACCESS.2026.3667181 |
| Access Level: | acceso abierto |
| Palabra clave: | Non-intrusive load monitoring (NILM) Electrical appliance classification Smart grid applications Feature extraction Machine learning |
| Sumario: | Non-Intrusive Load Monitoring (NILM) aims to identify electrical appliances in a household from voltage and current signals, enabling energy efficiency and smart grid applications. This study investigates NILM classification using the PLAID sub-metered dataset, evaluating multiple feature extraction techniques and classification algorithms. The results demonstrate that generic statistical features outperform domain-specific electrical features, including those based on IEEE standards. Feature analysis reveals that transient-state signals generally lead to higher classification accuracy than steady-state signals. However, the best performance is achieved by combining statistical features from both states, specifically by characterizing the steady-state current signal, complemented by its phase shift, and the active-state current signal filtered through nine pass-band filters. Dimensionality reduction further improves classification by enhancing feature relevance. Among the classifiers tested, Random Forest achieves the highest performance, reaching 93% accuracy under optimal conditions. To address dataset imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. While this technique does not improve overall performance, it enhances classification fairness by improving recognition of underrepresented categories at the cost of a slight decrease in the most populated ones. Learning curve analysis suggests that performance has not yet plateaued, indicating that a larger dataset would further enhance results. These findings highlight key challenges and opportunities in NILM, suggesting that future work should focus on expanding dataset diversity and refining feature extraction methods to improve classification robustness. |
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