Examinando por Autor "Fuentes, David"

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Architectural strategies for enhanced NILM classification and anomaly detection: Addressing limited data scenarios
(Elsevier - Expert Systems With Applications, 2025-04-24) Diego-Otón, Laura de; Hernández, Álvaro; Fuentes, David; Nieto, Rubén; Navarro, Víctor M.
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.
Monitoring Daily Activities in Households by Means of Energy Consumption Measurements from Smart Meters
(MDPI, 2025-02-27) Hernández, Álvaro; Nieto, Rubén; de Diego-Otón, Laura; Villadangos-Carrizo, José M.; Pizarro, Daniel; Fuentes, David; Pérez-Rubio, María C.
Non-Intrusive Load Monitoring (NILM) includes a set of methods orientated to disaggregating the power consumption of a household per appliance. It is commonly based on a single metering point, typically a smart meter at the entry of the electrical grid of the building, where signals of interest, such as voltage or current, can be measured and analyzed in order to disaggregate and identify which appliance is turned on/off at any time. Although this information is key for further applications linked to energy efficiency and management, it may also be applied to social and health contexts. Since the activation of the appliances in a household is related to certain daily activities carried out by the corresponding tenants, NILM techniques are also interesting in the design of remote monitoring systems that can enhance the development of novel feasible healthcare models. Therefore, these techniques may foster the independent living of elderly and/or cognitively impaired people in their own homes, while relatives and caregivers may have access to additional information about a person’s routines. In this context, this work describes an intelligent solution based on deep neural networks, which is able to identify the daily activities carried out in a household, starting from the disaggregated consumption per appliance provided by a commercial smart meter. With the daily activities identified, the usage patterns of the appliances and the corresponding behaviour can be monitored in the long term after a training period. In this way, every new day may be assessed statistically, thus providing a score about how similar this day is to the routines learned during the training interval. The proposal has been experimentally validated by means of two commercially available smart monitors installed in real houses where tenants followed their daily routines, as well as by using the well-known database UK-DALE.