Addressing climate change and promoting sustainable energy practices is a pressing issue
in our era and requires a comprehensive transformation of our energy production …

The large-scale roll-out of smart meters allows the collection of a vast amount of fine-grained
electricity consumption data. Once analyzed, such data can enable cutting-edge data-driven …

This article proposes a non-intrusive load monitoring (NILM) framework based on a deep
convolutional neural network (DCNN) to profile each household appliance on/off status and …

Non-intrusive load monitoring (NILM) provides fine-grained consumption information at the
appliance level by analyzing the terminal voltage and total current measured. It shows …

Non-intrusive load monitoring (NILM) techniques are central techniques to achieve the
energy sustainability goals through the identification of operating appliances in the …

Accurate identification of various loads for nonintrusive load monitoring (NILM) plays a
crucial role in a variety of power system applications and strategies. In this article, a novel …

With the increasing integration of renewable energy sources into distribution networks, short-
term voltage stability (STVS) has become a prominent issue. Considering grid impedance …

The performance of non-intrusive load monitoring (NILM) system is closely related to the
uniqueness and comprehensiveness of the load features extracted from the equipment …

This article presents an efficient home energy management system for residential household
within a microgrid. The smart meter's database initially stores the consumer's solar …

Abstract The discipline of Non-Intrusive Load Monitoring (NILM) has witnessed a surge in
the application of machine learning and pattern recognition approaches, enabling …