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Non-Intrusive Load Monitoring

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Showing 1–50 of 99 papers

TitleStatusHype
On time series representations for multi-label NILMCode1
MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled DataCode1
COLD: Concurrent Loads Disaggregator for Non-Intrusive Load MonitoringCode1
NILM as a regression versus classification problem: the importance of thresholdingCode1
NILMFormer: Non-Intrusive Load Monitoring that Accounts for Non-StationarityCode1
ELECTRIcity: An Efficient Transformer for Non-Intrusive Load MonitoringCode1
Energy Disaggregation using Variational AutoencodersCode1
Transfer Learning for Non-Intrusive Load MonitoringCode0
Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural NetworkCode0
Challenges in Gaussian Processes for Non Intrusive Load MonitoringCode0
On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load MonitoringCode0
Wavenilm: A causal neural network for power disaggregation from the complex power signalCode0
Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter SeriesCode0
Toward Explainable NILM: Real-Time Event-Based NILM Framework for High-Frequency DataCode0
Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data AugmentationCode0
Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance EvaluationCode0
Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home AppliancesCode0
Learning Task-Aware Energy Disaggregation: a Federated ApproachCode0
Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS—0
Divide-Conquer Transformer Learning for Predicting Electric Vehicle Charging Events Using Smart Meter Data—0
DP^2-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring—0
Edge-Optimized Deep Learning & Pattern Recognition Techniques for Non-Intrusive Load Monitoring of Energy Time Series—0
Efficient Localness Transformer for Smart Sensor-Based Energy Disaggregation—0
End-to-end NILM System Using High Frequency Data and Neural Networks—0
Enhancing Non-Intrusive Load Monitoring with Features Extracted by Independent Component Analysis—0
Event Detection for Non-intrusive Load Monitoring using Tukey s Fences—0
Event-driven Two-stage Solution to Non-intrusive Load Monitoring—0
Evolutionary Deep Nets for Non-Intrusive Load Monitoring—0
Exploring Bayesian Surprise to Prevent Overfitting and to Predict Model Performance in Non-Intrusive Load Monitoring—0
FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning—0
Federated Sequence-to-Sequence Learning for Load Disaggregation from Unbalanced Low-Resolution Smart Meter Data—0
Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection—0
FedNILM: Applying Federated Learning to NILM Applications at the Edge—0
Functional Intrusive Load Monitor (FILM): A Model-based Platform for Non-Intrusive Load Monitoring System Development—0
HiFAKES: High-frequency synthetic appliance signatures generator for non-intrusive load monitoring—0
IMG-NILM: A Deep learning NILM approach using energy heatmaps—0
Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations—0
Incorporating Coincidental Water Data into Non-intrusive Load Monitoring—0
Latent Tensor Factorization with Nonlinear PID Control for Missing Data Recovery in Non-Intrusive Load Monitoring—0
Low-Frequency Load Identification using CNN-BiLSTM Attention Mechanism—0
Machine Learning Approaches for Non-Intrusive Home Absence Detection Based on Appliance Electrical Use—0
More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring—0
MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring based on A Dual-CNN Model—0
Multi Label Restricted Boltzmann Machine for Non-Intrusive Load Monitoring—0
Multi-timescale Event Detection in Nonintrusive Load Monitoring based on MDL Principle—0
Neural Network Ensembles to Real-time Identification of Plug-level Appliance Measurements—0
Non-Intrusive Electrical Appliances Monitoring and Classification using K-Nearest Neighbors—0
Non-Intrusive Load Monitoring Based on Image Load Signatures and Continual Learning—0
Non-intrusive Load Monitoring based on Self-supervised Learning—0
Non-Intrusive Load Monitoring for Feeder-Level EV Charging Detection: Sliding Window-based Approaches to Offline and Online Detection—0
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