This paper presents a critical approach to the non-intrusive load monitoring (NILM) problem,
by thoroughly reviewing the experimental framework of both legacy and state-of-the-art …

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

This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep
neural networks to disaggregate appliances from low frequency data, ie, data with sampling …

Non-intrusive load monitoring (NILM) is the task of disaggregating the total power
consumption into its individual sub-components. Over the years, signal processing and …

Appliance energy consumption tracking in a building is one of the vital enablers of energy
and cost saving. An economical and viable solution would be to estimate individual …

With the widespread deployment of smart meters worldwide, quantification of energy used
by individual appliances via Non-Intrusive Load Monitoring (NILM), ie, virtual submetering, is …

Smart nonintrusive load monitoring (NILM) represents a cost‐efficient technology for
observing power usage in buildings. It tackles several challenges in transitioning into a more …

Non-intrusive load monitoring (NILM) based energy disaggregation is the decomposition of
a system's energy into the consumption of its individual appliances. Previous work on deep …

Providing the user with appliance-level consumption data is the core of each energy
efficiency system. To that end, non-intrusive load monitoring is employed for extracting …

Nonintrusive load monitoring (NILM) is a technique to disaggregate an appliance's load
consumption from the aggregate load in a house. Monitoring the energy behavior has …