Architectural strategies for enhanced NILM classification and anomaly detection: Addressing limited data scenarios
Non-intrusive load monitoring (NILM) enables appliance-level behaviour analysis by examining the aggregated electrical consumption signals. These techniques hold significant potential for applications ranging from electrical load management to remote human health monitoring. Despite its potential, N...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Alcalá (UAH) |
| Repositorio: | e_Buah Biblioteca Digital Universidad de Alcalá |
| Idioma: | inglés |
| OAI Identifier: | oai:ebuah.uah.es:10017/67557 |
| Acceso en línea: | http://hdl.handle.net/10017/67557 https://dx.doi.org/10.1016/j.eswa.2025.127756 |
| Access Level: | acceso abierto |
| Palabra clave: | Non-Intrusive Load Monitoring Appliance identification Anomaly detection Limited data scenarios Electrónica Electronics |
| Sumario: | Non-intrusive load monitoring (NILM) enables appliance-level behaviour analysis by examining the aggregated electrical consumption signals. These techniques hold significant potential for applications ranging from electrical load management to remote human health monitoring. Despite its potential, NILM faces challenges in adapting to evolving appliance baselines, including the integration of new devices or the replacement of existing ones. These challenges aggravate when dealing with a large number of appliances, or even more if there are overlapping energy consumption profiles, thus reducing the effectiveness of load monitoring techniques. In real-world scenarios, the scarcity of labelled data further intensifies these issues, increasing the risk of overfitting. This limits the ability of NILM models to generalise and perform effectively on unseen data. To address these limitations, this work presents some methods for accurately classifying known appliances while identifying unknown ones by using features derived from electrical current signals. The framework includes a feature extraction stage that explores neural networks with both supervised and unsupervised learning techniques to derive latent representations. Additionally, the appliance distinction stage optimises data distribution for recognised known appliances and evaluates two distinct approaches (a supervised method and a semi-supervised one) for detecting unseen appliances. Experimental evaluations demonstrate promising results, achieving over 95% accuracy for the supervised feature extraction method and 83% for the unsupervised one in classifying known appliances, even under limited data conditions. Furthermore, both approaches performed well in detecting unseen appliances, with detection rates exceeding 90% for the supervised classification method and 70% for the semi-supervised method for certain categories. |
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