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Non-Intrusive Load Monitoring

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Showing 51–75 of 99 papers

TitleStatusHype
Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS—0
Machine Learning Approaches for Non-Intrusive Home Absence Detection Based on Appliance Electrical Use—0
Efficient Localness Transformer for Smart Sensor-Based Energy Disaggregation—0
A Deep Learning Technique using Low Sampling rate for residential Non Intrusive Load Monitoring—0
FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning—0
Adversarial Energy Disaggregation for Non-intrusive Load Monitoring—0
Event-driven Two-stage Solution to Non-intrusive Load Monitoring—0
Adaptive Event Detection for Representative Load Signature Extraction—0
ProfileSR-GAN: A GAN based Super-Resolution Method for Generating High-Resolution Load Profiles—0
FedNILM: Applying Federated Learning to NILM Applications at the Edge—0
More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring—0
Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection—0
Trimming Feature Extraction and Inference for MCU-based Edge NILM: a Systematic Approach—0
A Data-Efficient Approach to Behind-the-Meter Solar Generation Disaggregation—0
A Novel Hybrid Deep Learning Approach for Non-Intrusive Load Monitoring of Residential Appliance Based on Long Short Term Memory and Convolutional Neural Networks—0
Unsupervised Disaggregation of Water Heater Load from Smart Meter Data Processing—0
A Federated Learning Framework for Non-Intrusive Load Monitoring—0
A Comprehensive Review on the NILM Algorithms for Energy Disaggregation—0
Quantification of Disaggregation Difficulty with Respect to the Number of Meters—0
Incorporating Coincidental Water Data into Non-intrusive Load Monitoring—0
Prior Flow Variational Autoencoder: A density estimation model for Non-Intrusive Load Monitoring—0
Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective—0
Sequence-to-Sequence Load Disaggregation Using Multi-Scale Residual Neural Network—0
Exploring Bayesian Surprise to Prevent Overfitting and to Predict Model Performance in Non-Intrusive Load Monitoring—0
A Non-Intrusive Load Monitoring Approach for Very Short Term Power Predictions in Commercial Buildings—0
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