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 …
[HTML][HTML] Artificial intelligence techniques for enabling Big Data services in distribution networks: A review
S Barja-Martinez, M Aragüés-Peñalba… - … and Sustainable Energy …, 2021 - Elsevier
Artificial intelligence techniques lead to data-driven energy services in distribution power
systems by extracting value from the data generated by the deployed metering and sensing …
systems by extracting value from the data generated by the deployed metering and sensing …
A novel non-intrusive load monitoring method based on ResNet-seq2seq networks for energy disaggregation of distributed energy resources integrated with …
The increasing effects of global warming and energy depletion have raised concerns about
the pollution caused by traditional oil and fossil energy usage. Distributed energy resources …
the pollution caused by traditional oil and fossil energy usage. Distributed energy resources …
Toward load identification based on the Hilbert transform and sequence to sequence long short-term memory
S Heo, H Kim - IEEE Transactions on Smart Grid, 2021 - ieeexplore.ieee.org
Load identification is a core concept in non-intrusive load monitoring (NILM). Through NILM
systems, users can check their home appliance usage habits and then adjust their behavior …
systems, users can check their home appliance usage habits and then adjust their behavior …
High performance classification model to identify ransomware payments for heterogeneous bitcoin networks
QA Al-Haija, AA Alsulami - Electronics, 2021 - mdpi.com
The Bitcoin cryptocurrency is a worldwide prevalent virtualized digital currency
conceptualized in 2008 as a distributed transactions system. Bitcoin transactions make use …
conceptualized in 2008 as a distributed transactions system. Bitcoin transactions make use …
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 …
A secure and privacy-preserving data aggregation and classification model for smart grid
Smart meters are rapidly installing by utility providers to improve the reliability and
performance of Smart Grid. Utility providers analyze real-time smart meter data to monitor …
performance of Smart Grid. Utility providers analyze real-time smart meter data to monitor …
A smart home energy management system utilizing neurocomputing-based time-series load modeling and forecasting facilitated by energy decomposition for smart …
The key advantage of using power-utility-owned smart meters is the ability to transmit
electrical energy consumption data to power utilities' remote data centers for various …
electrical energy consumption data to power utilities' remote data centers for various …
Non-intrusive adaptive load identification based on siamese network
The traditional non-intrusive load monitoring (NILM) algorithms are mostly based on
classification models, which have several deficiencies. Firstly, a large amount of labeled …
classification models, which have several deficiencies. Firstly, a large amount of labeled …
[HTML][HTML] An IoT deep learning-based home appliances management and classification system
The rise in household energy consumption globally has increased the necessity for effective
electricity consumption management and load monitoring. Smart meters can facilitate fine …
electricity consumption management and load monitoring. Smart meters can facilitate fine …