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Improving Event-Based Non-Intrusive Load Monitoring Using Graph Signal Processing
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s11708-015-0358-6openalex-snapshot
A systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring
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A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring
inferredcites →https://doi.org/10.1016/j.comcom.2016.12.020openalex-snapshot
Spectrum-aware bio-inspired routing in cognitive radio sensor networks for smart grid applications
inferredcites →https://doi.org/10.1016/j.csi.2017.03.003openalex-snapshot
Capacity and spectrum-aware communication framework for wireless sensor network-based smart grid applications
inferredcites →https://doi.org/10.1016/j.enbuild.2016.08.050openalex-snapshot
Non-intrusive load disaggregation with adaptive estimations of devices main power effects and two-way interactions
inferredcites →https://doi.org/10.1016/j.enpol.2012.08.062openalex-snapshot
Is disaggregation the holy grail of energy efficiency? The case of electricity
inferredcites →https://doi.org/10.1038/sdata.2016.122openalex-snapshot
An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study
inferredcites →https://doi.org/10.1109/access.2016.2523803openalex-snapshot
Human Upper Limb Motion Analysis for Post-Stroke Impairment Assessment Using Video Analytics
inferredcites →https://doi.org/10.1109/access.2016.2557460openalex-snapshot
On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing
inferredcites →https://doi.org/10.1109/ecticon.2016.7561398openalex-snapshot
Nonintrusive load monitoring (NILM) using an Artificial Neural Network in embedded system with low sampling rate
inferredcites →https://doi.org/10.1109/eeeic.2015.7165334openalex-snapshot
Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overview
inferredcites →https://doi.org/10.1109/globalsip.2014.7032244openalex-snapshot
Signal denoising on graphs via graph filtering
inferredcites →https://doi.org/10.1109/icassp.2015.7178380openalex-snapshot
Optimal graph laplacian regularization for natural image denoising
inferredcites →https://doi.org/10.1109/icip.2013.6738252openalex-snapshot
Bilateral filter: Graph spectral interpretation and extensions
inferredcites →https://doi.org/10.1109/icmla.2012.249openalex-snapshot
Unsupervised Disaggregation for Non-intrusive Load Monitoring
inferredcites →https://doi.org/10.1109/indicon.2016.7839109openalex-snapshot
Data-driven electrical load disaggregation using graph signal processing
inferredcites →https://doi.org/10.1109/msp.2012.2235192openalex-snapshot
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
inferredcites →https://doi.org/10.1109/percom.2012.6199866openalex-snapshot
Leveraging smart meter data to recognize home appliances
inferredcites →https://doi.org/10.1109/percomw.2015.7134051openalex-snapshot
Autonomous load disaggregation approach based on active power measurements
inferredcites →https://doi.org/10.1109/pesgm.2015.7286502openalex-snapshot
Residential electric load disaggregation for low-frequency utility applications
inferredcites →https://doi.org/10.1109/secon.2010.5508244openalex-snapshot
Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor
inferredcites →https://doi.org/10.1109/smartgridcomm.2014.7007702openalex-snapshot
Non-intrusive appliance load monitoring using low-resolution smart meter data
inferredcites →https://doi.org/10.1109/tce.2011.5735484openalex-snapshot
Nonintrusive appliance load monitoring: Review and outlook
inferredcites →https://doi.org/10.1109/tim.2013.2278596openalex-snapshot
Electrical Signal Source Separation Via Nonnegative Tensor Factorization Using On Site Measurements in a Smart Home
inferredcites →https://doi.org/10.1109/tim.2014.2344373openalex-snapshot
PALDi: Online Load Disaggregation via Particle Filtering
inferredcites →https://doi.org/10.1109/tsg.2012.2185522openalex-snapshot
An Event Window Based Load Monitoring Technique for Smart Meters
inferredcites →https://doi.org/10.1109/tsg.2015.2462719openalex-snapshot
Approach in Nonintrusive Type I Load Monitoring Using Subtractive Clustering
inferredcites →https://doi.org/10.1109/tsg.2015.2494592openalex-snapshot
Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring
inferredcites →https://doi.org/10.1109/tsg.2016.2598872openalex-snapshot
Non-Intrusive Load Disaggregation Using Graph Signal Processing
inferredcites →https://doi.org/10.1109/tsg.2018.2826844openalex-snapshot
A Cloud-Based On-Line Disaggregation Algorithm for Home Appliance Loads
inferredcites →https://doi.org/10.1109/tsp.2013.2238935openalex-snapshot
Discrete Signal Processing on Graphs
inferredcites →https://doi.org/10.1109/tsp.2014.2321121openalex-snapshot
Discrete Signal Processing on Graphs: Frequency Analysis
inferredcites →https://doi.org/10.1137/1.9781611972818.64openalex-snapshot
Unsupervised Disaggregation of Low Frequency Power Measurements
inferredcites →https://doi.org/10.1155/2017/4216281openalex-snapshot
Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature
inferredcites →https://doi.org/10.1184/r1/6603563openalex-snapshot
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation
inferredcites →https://doi.org/10.1609/aaai.v26i1.8162openalex-snapshot
Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types
inferredcites →https://doi.org/10.1609/aaai.v32i1.11873openalex-snapshot
Sequence-to-Point Learning With Neural Networks for Non-Intrusive Load Monitoring
inferredcites →https://doi.org/10.3390/fi8010004openalex-snapshot
Context-Based Energy Disaggregation in Smart Homes
inferredcites →https://doi.org/10.3390/s121216838openalex-snapshot
Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey
inferredcites →https://doi.org/10.3934/energy.2016.1.1openalex-snapshot
Low-complexity energy disaggregation using appliance load modelling
inferredcites →https://doi.org/10.48550/arxiv.1510.09130openalex-snapshot
Latent Bayesian melding for integrating individual and population models
inferredcites →https://doi.org/10.48550/arxiv.1605.00962openalex-snapshot
Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals
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Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions
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A Review on Non-intrusive Load Monitoring Approaches Based on Machine Learning
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Non-intrusive Load Monitoring and Its Application in Energy Flexibility Potential Extraction of Active Buildings
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Weakly Supervised Transfer Learning for Multi-label Appliance Classification
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Research Review of Non-invasive Load Monitoring
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Curvature scale space-based signatures for electrical load classification in NILM
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New parallel hybrid PHCNN-GRU deep learning model for multi-output NILM disaggregation
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Data Model Compression Method Based on Iterative Pruning and Distillation for Edge Non-Intrusive Load Monitoring
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Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?
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Implementation of a robust real-time non-intrusive load monitoring solution
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Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree
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Non-intrusive load disaggregation solutions for very low-rate smart meter data
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Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction
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Toward smart energy user: Real time non-intrusive load monitoring with simultaneous switching operations
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An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem
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Unsupervised separation of the thermosensitive contribution in the power consumption at a country scale
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Non-intrusive thermal load disaggregation and forecasting for effective HVAC systems
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Non-intrusive load decomposition based on CNN–LSTM hybrid deep learning model
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Multi-label load disaggregation in presence of non-targeted loads
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Analysis of supervised graph signal processing-based load disaggregation for residential demand-side management
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A robust non-intrusive load disaggregation method with roof-top photovoltaics
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Electric energy disaggregation via non-intrusive load monitoring: A state-of-the-art systematic review
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Applications of NILM Techniques to Energy Management and Assisted Living
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EOLD: A reinforcement learning-based energy-optimised load disaggregation framework for demand-side energy management
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Reproducible non-intrusive load monitoring: A survey of open-source methods and practical challenges
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Incorporating coincidental water data into non-intrusive load monitoring
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Renewable-based microgrids’ energy management using smart deep learning techniques: Realistic digital twin case
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The Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece
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Fed‐NILM: A federated learning‐based non‐intrusive load monitoring method for privacy‐protection
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New Method of Transformer Differential Protection Based on Graph Fourier Transform
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A Feature Extraction Method for Non-intrusive Load Identification
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Multi-state household appliance identification based on neural network
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Multi-Channel Recurrent Convolutional Neural Networks for Energy Disaggregation
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Training-Free Non-Intrusive Load Extracting of Residential Electric Vehicle Charging Loads
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Load Disaggregation Based on Time Window for HEMS Application
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Abnormal Battery Identification via Graph Signal Processing Method
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Incremental Classifier Learning for Non-intrusive Appliance Identification
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Universal Non-Intrusive Load Monitoring (UNILM) Using Filter Pipelines, Probabilistic Knapsack, and Labelled Partition Maps
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Nonintrusive Load Monitoring Algorithm Using SOM-AdaDBSCAN
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A Comprehensive Review and Outlook of Non-Intrusive Load Monitoring Systems
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HeartDIS: A Generalizable End-to-End Energy Disaggregation Pipeline
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Apply Graph Signal Processing on NILM: An Unsupervised Approach Featuring Power Sequences
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Event Matching Classification Method for Non-Intrusive Load Monitoring
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A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
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Quantification of Disaggregation Difficulty with Respect to the Number of Meters
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A Federated Learning Framework for Non-Intrusive Load Monitoring
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Data-Flow Programming based Non-Intrusive Load Monitoring for Electricity in Remote Area
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