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Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification
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1 of the 51 references listed on this page is in the Retraction Watch database as retracted (export 2026-11-04): 10.1016/j.rser.2016.07.009 (notice 2020-11-26).
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-642-20267-4_4openalex-snapshot
An Experimental Study on Electrical Signature Identification of Non-Intrusive Load Monitoring (NILM) Systems
inferredcites →https://doi.org/10.1007/978-3-642-28942-2_29openalex-snapshot
On Ensemble Classifiers for Nonintrusive Appliance Load Monitoring
inferredcites →https://doi.org/10.1007/978-3-642-37192-9_19openalex-snapshot
Evolving Non-Intrusive Load Monitoring
inferredcites →https://doi.org/10.1016/j.apenergy.2016.10.040openalex-snapshot
A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring
inferredcites →https://doi.org/10.1016/j.enbuild.2017.11.054openalex-snapshot
Denoising autoencoders for Non-Intrusive Load Monitoring: Improvements and comparative evaluation
inferredcites →https://doi.org/10.1016/j.enbuild.2018.11.013openalex-snapshot
A NILM algorithm with enhanced disaggregation scheme under harmonic current vectors
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/10.1016/j.epsr.2017.12.012openalex-snapshot
Non-intrusive load monitoring algorithm based on features of V–I trajectory
inferredcites →https://doi.org/10.1016/j.epsr.2019.01.034openalex-snapshot
Non-intrusive load disaggregation based on deep dilated residual network
inferredcites →https://doi.org/10.1016/j.neucom.2015.03.088openalex-snapshot
Analysis of trends in seasonal electrical energy consumption via non-negative tensor factorization
inferredcites →https://doi.org/10.1016/j.rser.2016.07.009openalex-snapshot
RETRACTED: A review disaggregation method in Non-intrusive Appliance Load Monitoring
inferredcites →https://doi.org/10.1016/j.rser.2017.05.096openalex-snapshot
Non-intrusive load monitoring through home energy management systems: A comprehensive review
inferredcites →https://doi.org/10.1038/sdata.2015.7openalex-snapshot
The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
inferredcites →https://doi.org/10.1109/cvpr.2019.00949openalex-snapshot
Class-Balanced Loss Based on Effective Number of Samples
inferredcites →https://doi.org/10.1109/globalsip.2015.7418157openalex-snapshot
A new approach for supervised power disaggregation by using a deep recurrent LSTM network
inferredcites →https://doi.org/10.1109/sege.2018.8499519openalex-snapshot
Regularized LSTM Based Deep Learning Model: First Step towards Real-Time Non-Intrusive Load Monitoring
inferredcites →https://doi.org/10.1109/tce.2010.5606276openalex-snapshot
Smart home energy management system using IEEE 802.15.4 and zigbee
inferredcites →https://doi.org/10.1109/tce.2017.015014openalex-snapshot
A smart home energy management system using IoT and big data analytics approach
inferredcites →https://doi.org/10.1109/tce.2018.2843292openalex-snapshot
Nonintrusive Load Monitoring Algorithm Using Mixed-Integer Linear Programming
inferredcites →https://doi.org/10.1109/tcst.2015.2476777openalex-snapshot
Sparse Optimization for Automated Energy End Use Disaggregation
inferredcites →https://doi.org/10.1109/tia.2013.2283318openalex-snapshot
Power-Spectrum-Based Wavelet Transform for Nonintrusive Demand Monitoring and Load Identification
inferredcites →https://doi.org/10.1109/tii.2016.2590359openalex-snapshot
Improving Nonintrusive Load Monitoring Efficiency via a Hybrid Programing Method
inferredcites →https://doi.org/10.1109/tim.2013.2289700openalex-snapshot
Development of an Improved Time–Frequency Analysis-Based Nonintrusive Load Monitor for Load Demand Identification
inferredcites →https://doi.org/10.1109/tsg.2013.2271282openalex-snapshot
An Empirical Investigation of V-I Trajectory Based Load Signatures for Non-Intrusive Load Monitoring
inferredcites →https://doi.org/10.1109/tsg.2014.2373314openalex-snapshot
Feature Extraction for Load Identification Using Long-Term Operating Waveforms
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.2016.2631238openalex-snapshot
An Extensible Approach for Non-Intrusive Load Disaggregation With Smart Meter Data
inferredcites →https://doi.org/10.1109/tsg.2018.2818167openalex-snapshot
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
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/10.1609/aaai.v28i1.8769openalex-snapshot
Contextually Supervised Source Separation with Application to Energy Disaggregation
inferredcites →https://doi.org/10.1609/aaai.v29i1.9249openalex-snapshot
Energy Disaggregation via Learning Powerlets and Sparse Coding
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/app8040542openalex-snapshot
An Iterative Load Disaggregation Approach Based on Appliance Consumption Pattern
inferredcites →https://doi.org/10.3390/app9173558openalex-snapshot
Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration
inferredcites →https://doi.org/10.3390/app9245363openalex-snapshot
Non-Intrusive Load Monitoring Using Current Shapelets
inferredcites →https://doi.org/10.3390/electronics7100235openalex-snapshot
An Extreme Learning Machine Approach to Effective Energy Disaggregation
inferredcites →https://doi.org/10.3390/en10040538openalex-snapshot
Load Signature Formulation for Non-Intrusive Load Monitoring Based on Current Measurements
inferredcites →https://doi.org/10.3390/en12071371openalex-snapshot
A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks
inferredcites →https://doi.org/10.3390/en12081572openalex-snapshot
Concatenate Convolutional Neural Networks for Non-Intrusive Load Monitoring across Complex Background
inferredcites →https://doi.org/10.3390/en12091797openalex-snapshot
A Novel Nonintrusive Load Monitoring Approach based on Linear-Chain Conditional Random Fields
inferredcites →https://doi.org/10.3390/en12112203openalex-snapshot
NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review
inferredcites →https://doi.org/10.3390/en12142725openalex-snapshot
Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector
inferredcites →https://doi.org/10.3390/en12142804openalex-snapshot
Appliance Classification by Power Signal Analysis Based on Multi-Feature Combination Multi-Layer LSTM
inferredcites →https://doi.org/10.3390/en5114569openalex-snapshot
Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses
inferredcites →https://doi.org/10.3390/en7117041openalex-snapshot
An Efficient, Scalable Time-Frequency Method for Tracking Energy Usage of Domestic Appliances Using a Two-Step Classification Algorithm
inferredcites →https://doi.org/10.3390/en8099029openalex-snapshot
Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System
inferredcites →https://doi.org/10.3390/pr7060337openalex-snapshot
Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System
inferredcites →https://doi.org/10.3390/s19235236openalex-snapshot
Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm
inferredcites →https://doi.org/10.5391/ijfis.2012.12.3.193openalex-snapshot
Electric Load Signature Analysis for Home Energy Monitoring System
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Non-intrusive Load Monitoring and Its Application in Energy Flexibility Potential Extraction of Active Buildings
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Estimating the Electrical Power Output of Industrial Devices with End-to-End Time-Series Classification in the Presence of Label Noise
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Weakly Supervised Transfer Learning for Multi-label Appliance Classification
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A Scoping Review of Energy Load Disaggregation
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Identifying Electricity Consumption of Individual Household Devices by Using Smart Meter Data and AI Technologies
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A Federated Learning Method for Non-intrusive Load Monitoring Based on Fed-Prox and Bi-GRU
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A comprehensive review of machine learning and deep learning models for non-intrusive load monitoring: performance, analyses, practical insights, and emerging trends
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Thresholding methods in non-intrusive load monitoring
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New hybrid deep learning models for multi-target NILM disaggregation
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An unsupervised method of HVAC energy disaggregation and demand response potential estimation
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An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem
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Fine-tuning non-intrusive load monitoring model through user interaction: A practical approach to appliance recognition with limited labeled data
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A novel discrete deep learning-based intelligent methodology for energy consumption classification
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Research on non-intrusive load decomposition model based on parallel multi-scale attention mechanism and its application in smart grid
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XNILMBoost: Explainability-informed load disaggregation training enhancement using attribution priors
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Electric energy disaggregation via non-intrusive load monitoring: A state-of-the-art systematic review
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EOLD: A reinforcement learning-based energy-optimised load disaggregation framework for demand-side energy management
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DP 2 -NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring
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Reproducible non-intrusive load monitoring: A survey of open-source methods and practical challenges
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Anomaly detection on household appliances based on variational autoencoders
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Low frequency-based energy disaggregation using sliding windows and deep learning
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Effective Feature Selection and Deep Learning-Based Classification for Non-Intrusive Load Monitoring
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An intelligent non-intrusive load monitoring model based on power encoding and convolutional state modules
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Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical Integration
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Load Disaggregation Based on Time Window for HEMS Application
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Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus Clustering
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Contextual Sequence-to-Point Deep Learning for Household Energy Disaggregation
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Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances
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Design and Stage Analysis of a Non-Invasive Monitoring System for Determining Formaldehyde Release From Wood-Based Panels by the Chamber Method
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Sequence to Sequence Load Recognition Model Based On Sparse Self-attention Transformer
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Machine Learning Techniques for Non-Intrusive Load Monitoring for Enhanced Energy Predictions
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Comparison of Appliance Signature Classification Methods for Non-Intrusive Load Monitoring
inferred← citeshttps://doi.org/10.1109/eeeic/icpseurope57605.2023.10194770openalex-snapshot
Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review
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Decomposition Model of Local Cross-Channel Interaction Load and Its Application in PV-Storage Microgrids
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Artificial Intelligence (AI)-based identification of appliances in households through NILM
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Assessment of Non-Intrusive Load Monitoring as a Blind Source Separation Problem
inferred← citeshttps://doi.org/10.1109/hdis56859.2022.9991439openalex-snapshot
TransNILM: A Transformer-based Deep Learning Model for Non-intrusive Load Monitoring
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Continilm: A Continual Learning Scheme for Non-Intrusive Load Monitoring
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Impact of Normalization on BiLSTM Based Models for Energy Disaggregation
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Neural NILM Learning Paradigms: From Centralised to Decentralised Learning
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Comprehensive Analysis of Convolutional Neural Network Models for Non-Instructive Load Monitoring
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Non-intrusive load disaggregation via a fully convolutional neural network: improving the accuracy on unseen household
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Contextual Pooling for Multiple-Instance Learning-based Non-Intrusive Load Monitoring
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Improving Load Forecast Accuracy of Households Using Load Disaggregation Techniques
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Explaining the Decisions of Deep Learning Models for Load Disaggregation (NILM) Based on XAI
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Non-Intrusive Load Monitoring using Multi-Output CNNs
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COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring
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MOGA designed neural networks for non-intrusive load monitoring
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Deep Sequence-to-point Learning for Electric Appliance Energy Disaggregation in Smart Building
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SAMNet: Toward Latency-Free Non-Intrusive Load Monitoring via Multi-Task Deep Learning
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Non-Intrusive Load Monitoring: A Review
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Multilabel Appliance Classification With Weakly Labeled Data for Non-Intrusive Load Monitoring
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Load Recognition in Hardware-Based Low Voltage Distribution Grids Using Convolutional Neural Networks
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CALI: A Comprehensive Multi-Type and Multi-Unit Appliance Load Identification Framework
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Appliance recognition with combined single- and multi-label approaches
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NILMFormer: Non-Intrusive Load Monitoring that Accounts for Non-Stationarity
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Thresholding Methods in Non-Intrusive Load Monitoring to Estimate Appliance Status
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Deep learning based non-intrusive load monitoring with low resolution data from smart meters
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A weakly supervised active learning framework for non-intrusive load monitoring
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A Survey of Traditional and Emerging Deep Learning Techniques for Non-Intrusive Load Monitoring
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Multi-Label Classification Based on Associations
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Nonintrusive Load Monitoring (NILM) Using a Deep Learning Model with a Transformer-Based Attention Mechanism and Temporal Pooling
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Performance Enhancement of Non-Intrusive Load Monitoring Based on Adaptive Multi-Scale Attention Integration Module
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A 28 nm FD-SOI Current-Mode Synaptic Weighting Cell for Low-Complexity Event-Based NILM MLP Inference
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Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network
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Review on Deep Neural Networks Applied to Low-Frequency NILM
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Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid
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Scattering Transform for Classification in Non-Intrusive Load Monitoring
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A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements
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Voltage-Based Load Recognition in Low Voltage Distribution Grids with Deep Learning
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Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection
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Energy Disaggregation Using Multi-Objective Genetic Algorithm Designed Neural Networks
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Non-Intrusive Load Monitoring Based on Multiscale Attention Mechanisms
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Non-Intrusive Load Monitoring of Buildings Using Spectral Clustering
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A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation
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Deep Learning-Based Non-Intrusive Commercial Load Monitoring
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Rule-Based Non-Intrusive Load Monitoring Using Steady-State Current Waveform Features
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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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Sistema de monitoramento energético compreensível para aplicação em cidades inteligentes
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A Novel Event-based Non-intrusive Load Monitoring Algorithm
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NILM as a regression versus classification problem: the importance of thresholding
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