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 …
Non-intrusive load monitoring: A review
PA Schirmer, I Mporas - IEEE Transactions on Smart Grid, 2022 - ieeexplore.ieee.org
The rapid development of technology in the electrical energy sector within the last 20 years
has led to growing electric power needs through the increased number of electrical …
has led to growing electric power needs through the increased number of electrical …
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 …
Adaptive weighted recurrence graphs for appliance recognition in non-intrusive load monitoring
A Faustine, L Pereira… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
To this day, hyperparameter tuning remains a cumbersome task in Non-Intrusive Load
Monitoring (NILM) research, as researchers and practitioners are forced to invest a …
Monitoring (NILM) research, as researchers and practitioners are forced to invest a …
UNet-NILM: A deep neural network for multi-tasks appliances state detection and power estimation in NILM
Over the years, an enormous amount of research has been exploring Deep Neural Networks
(DNN), particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks …
(DNN), particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks …
A synthetic energy dataset for non-intrusive load monitoring in households
C Klemenjak, C Kovatsch, M Herold, W Elmenreich - Scientific data, 2020 - nature.com
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 …
mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling …
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 …
[HTML][HTML] Urban water consumption at multiple spatial and temporal scales. A review of existing datasets
Over the last three decades, the increasing development of smart water meter trials and the
rise of demand management has fostered the collection of water demand data at …
rise of demand management has fostered the collection of water demand data at …
PB-NILM: Pinball guided deep non-intrusive load monitoring
The work in this paper proposes the application of the pinball quantile loss function to guide
a deep neural network for Non-Intrusive Load Monitoring. The proposed architecture …
a deep neural network for Non-Intrusive Load Monitoring. The proposed architecture …
Multi-label learning for appliance recognition in NILM using Fryze-current decomposition and convolutional neural network
A Faustine, L Pereira - Energies, 2020 - mdpi.com
The advance in energy-sensing and smart-meter technologies have motivated the use of a
Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end …
Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end …