NILM applications: Literature review of learning approaches, recent developments and challenges
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 …
by thoroughly reviewing the experimental framework of both legacy and state-of-the-art …
Review on deep neural networks applied to low-frequency nilm
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 …
neural networks to disaggregate appliances from low frequency data, ie, data with sampling …
Towards trustworthy energy disaggregation: A review of challenges, methods, and perspectives for non-intrusive load monitoring
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 …
consumption into its individual sub-components. Over the years, signal processing and …
Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions
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 …
observing power usage in buildings. It tackles several challenges in transitioning into a more …
A scalable real-time non-intrusive load monitoring system for the estimation of household appliance power consumption
Smart-meter technology advancements have resulted in the generation of massive volumes
of information introducing new opportunities for energy services and data-driven business …
of information introducing new opportunities for energy services and data-driven business …
A review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (NILM) in residential context
The rising demand for energy conservation in residential buildings has increased interest in
load monitoring techniques by exploiting energy consumption data. In recent years …
load monitoring techniques by exploiting energy consumption data. In recent years …
FPSeq2Q: Fully parameterized sequence to quantile regression for net-load forecasting with uncertainty estimates
The increased penetration of Renewable Energy Sources (RES) as part of a decentralized
and distributed power system makes net-load forecasting a critical component in the …
and distributed power system makes net-load forecasting a critical component in the …
Watt's up at home? Smart meter data analytics from a consumer-centric perspective
The key advantage of smart meters over traditional metering devices is their ability to
transfer consumption information to remote data processing systems. Besides enabling the …
transfer consumption information to remote data processing systems. Besides enabling the …
Nonintrusive load monitoring using an LSTM with feedback structure
H Hwang, S Kang - IEEE Transactions on Instrumentation and …, 2022 - ieeexplore.ieee.org
Many non-intrusive load monitoring (NILM) studies use high-frequency data to classify the
device's ON/OFF state. However, these approaches cannot be applied in real-world …
device's ON/OFF state. However, these approaches cannot be applied in real-world …
Multilabel appliance classification with weakly labeled data for non-intrusive load monitoring
Non-Intrusive Load Monitoring consists in estimating the power consumption or the states of
the appliances using electrical parameters acquired from a single metering point. State-of …
the appliances using electrical parameters acquired from a single metering point. State-of …