Apply Graph Signal Processing on NILM: An Unsupervised Approach Featuring Power Sequences. [PDF]
Zhao B, Li X, Luan W, Liu B.
europepmc +1 more source
A resource-efficient machine learning framework for real-time non-intrusive load monitoring and performance optimization in solar-powered aviation systems. [PDF]
Echarif AM +7 more
europepmc +1 more source
Optimized statistical test for event detection in non-intrusive load monitoring [PDF]
De Baets, Leen +4 more
core +1 more source
Federated learning-enhanced generative models for non-intrusive load monitoring in smart homes. [PDF]
Lu Y, Xu S, Liu Y, Jiang X.
europepmc +1 more source
Deep Adaptive Ensemble Filter for Non-Intrusive Residential Load Monitoring. [PDF]
Kianpoor N, Hoff B, Østrem T.
europepmc +1 more source
Multimodal Utility Data for Appliance Recognition: A Case Study with Rule-Based Algorithms. [PDF]
Orłowski A +3 more
europepmc +1 more source
Confidence-Based, Collaborative, Distributed Continual Learning Framework for Non-Intrusive Load Monitoring in Smart Grids. [PDF]
Lan C, Luo Q, Yu T, Liang M, Pan Z.
europepmc +1 more source
Capturing High-Frequency Harmonic Signatures for NILM: Building a Dataset for Load Disaggregation. [PDF]
Dinar F, Paris S, Busvelle É.
europepmc +1 more source
On enabling collaborative non-intrusive load monitoring for sustainable smart cities. [PDF]
Shi Y +5 more
europepmc +1 more source
Federated learning-based non-intrusive load monitoring adaptive to real-world heterogeneities. [PDF]
Luo Q +5 more
europepmc +1 more source

