To reflect the future electricity demand variations in the industrial sector of western China
under the “carbon peaking” and “carbon neutral” strategies, that the traditional methods of …

Non-intrusive load monitoring (NILM), as an important part of intelligent electricity
consumption, improves the cognitive level of the load by analyzing the bus power in a …

Non-intrusive load monitoring is the main trend of green energy-saving electricity
consumption at present, and load identification is a core part of non-invasive load …

In order to improve the efficiency of distributed generator configuration calibration, a
distributed generator configuration calibration method based on attention mechanism and …

Sažetak The implementation of non-intrusive load monitoring has gained significant
attention as a promising solution for disaggregating and identifying individual appliances' …

This study developed a specialized load identification model for a specific ocean platform
that integrates advanced deep learning techniques with the unique physical characteristics …

Traditional non-intrusive load monitoring (NILM) methods rely on massive historical labeled
data. However, due to the privacy and high labeling cost of datasets, their generality and …

The exponential increase in energy demands continuously causes high price energy tariffs
for domestic and commercial consumers. To overcome this problem, researchers strive to …

Non invasive load monitoring (NILM) is beneficial for enhancing the monitoring capability of
the distribution network and is crucial for improving the safety of smart grid operation …

Cognitive green computing (CGC) is widely used in the Internet of Things (IoT) for the smart
city. As the power system of the smart city, the smart grid has benefited from CGC, which can …