The rising demand for energy conservation in residential buildings has increased interest in
load monitoring techniques by exploiting energy consumption data. In recent years …

Energy load disaggregation can contribute to balancing power grids by enhancing the
effectiveness of demand-side management and promoting electricity-saving behavior …

Abstract Deep Domain Adaptation (DDA), which transfers the knowledge learned in the
source domain to the target domain, has made remarkable achievements in intelligent fault …

Load event matching is the key for event-based non-intrusive load monitoring (NILM). It aims
to find the load event sequence corresponding to the appliance's operation cycle from all …

This study evaluates the effect of complete nationwide lockdown in 2020 on residential
electricity demand across 13 Indian cities and the role of digitalisation using a public smart …

In IoT load monitoring system of the smart grid, the non-intrusive load monitoring and
identification (NILMI) has become the research focus. However, the existing researches …

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

To achieve the goal of peaking carbon emissions globally and carbon neutrality, smart
energy management is a promising way to boost energy conservation and estimate the …

The knowledge of the users' electricity consumption pattern is an important coordinating
mechanism between the utility company and the electricity consumers in terms of key …

Due to the diversity of appliances and users' power consumption behaviors, it is challenging
to accurately extract load signature samples for non-intrusive load monitoring (NILM) in …