Non‐intrusive load monitoring (also known as NILM or energy disaggregation) is the
process of estimating the energy consumption of individual appliances from electric power …

Abstract Nowadays Non-Intrusive Load Monitoring (NILM) is considered a hot topic among
researchers. The energy disaggregation datasets are used as the benchmark to validate the …

Research on smart grid technologies is expected to result in effective climate change
mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling …

Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into
home appliance electricity consumption using only the building's smart meter. Most current …

In the recent years, there has been an increasing academic and industrial interest for
analyzing the electrical consumption of commercial buildings. Whilst having similarities with …

Smart Energy Applications are particularly impacting, especially due to energy resource
scarcity and its high associated costs. Smart management of energy consumption derives …

Electrical consumption data contain a wealth of information, and their collection at scale is
facilitated by the deployment of smart meters. Data collected this way is an aggregation of …

As households are equipped with smart meters, supervised Machine Learning (ML) models
and especially Non-Intrusive Load Monitoring (NILM) disaggregation algorithms are …

Recently, an increasing number of Artificial Intelligence services have been developed for a
variety of domains. Machine Learning and especially Deep Learning services require a …

Abstract The application of Deep Learning methodologies to Non-Intrusive Load Monitoring
(NILM) gave rise to a new family of Neural NILM approaches which increasingly outperform …