Energy detection is an important part of intelligent power consumption, and its key
technology is non-intrusive load monitoring (NILM). In this study, an end-to-end model is …

It is known that successful load disaggregation via deep learning relies on a large number of
labeled data to train the deep neural networks. However, it is hard and expensive to acquire …

Mounting concerns pertaining to energy efficiency have led to the research of load
monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the …

This project will address the energy consumption disaggregation problem through the
design of intelligent systems, based on deep artificial neural networks, which would be part …

Energy disaggregation forecasts the consumption of individual appliance from a single
meter that records a household's overall electricity requirement. Although the appliance's …

If a smart grid is to be described in one word, that word would be'connectivity'. While
electricity production and consumption still depend on a limited number of physical …

Residential smart meters have been widely installed in urban houses nationwide to provide
efficient and responsive monitoring and billing for consumers. Studies have shown that …

The oncoming modernization process of the power grids, driven above all by
decarbonisation objectives and the continuous improvement of digital technologies, is …

The increasing deployment of energy management systems (EMS) is assisting end-users to
become more aware of their energy consumption, intending to mitigate energy waste. In this …

Non-intrusive load monitoring (NILM) or energy disaggregation is an inverse problem
whereby the goal is to extract the load profiles of individual appliances, given an aggregate …