Preliminary Unknown Appliance Detection Using Convolutional Variational Auto-Encoders for AAL

The healthcare landscape is evolving, emphasising the importance of Active Assisted Living in addressing challenges presented by the ageing population. Integrating Non-Intrusive Load Monitoring provides insights into individual appliance usage patterns without additional sensors, enabling the detection of changes in daily activities that may reflect a person's health status. Within the effectiveness of this approach, adaptability is crucial as households evolve, discerning variations in energy consumption patterns caused by new or replaced electrical devices. For this reason, this work introduces a sophisticated hybrid approach combining deep learning and classic machine learning to classify household electrical loads and detect unknown loads using high-frequency electrical current signal data. Finally, while experimental results highlight areas for improvement, the findings suggest promising applications in household settings, marking a step forward in facing hurdles within the AAL context.