In recent years, the development of smart sustainable cities has become the primary focus
among urban planners and policy makers to make responsible use of resources, conserve …

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 …

Energy disaggregation estimates appliance-by-appliance electricity consumption from a
single meter that measures the whole home's electricity demand. Recently, deep neural …

Demand-side management technology is a key element of the proposed smart grid, which
will help utilities make more efficient use of their generation assets by reducing consumers' …

This paper aims to address two timely energy problems. First, significant low-cost energy
reductions can be made in the residential and commercial sectors, but these savings have …

Energy disaggregation is the task of segregating the aggregate energy of the entire building
(as logged by the smart-meter) into the energy consumed by individual appliances. This is a …

Based upon the analysis of load signatures, this paper presents a nonintrusive load
monitoring (NILM) technique. With a characterizing response associated with a transient …

Energy management systems strive to use energy resources efficiently, save energy, and
reduce carbon output. This study proposes transient feature analyses of the transient …

To address privacy concerns of state-of-the-art centralized machine learning in non-intrusive
load monitoring (NILM) applications, the adoption of federated learning (FL) has emerged as …

This paper presents a technique to extract load signatures non-intrusively by using the smart
meter data. Load signature extraction is different from load activity identification. It is a new …