Conditioned fully convolutional denoising autoencoder for multi-target NILM

Συγγραφείς: García, Diego, Pérez, Daniel, Papapetrou, Panagiotis, 1981, Díaz, Ignacio, Cuadrado, Abel A., Enguita, José M., Domínguez, Manuel

Πηγή: Neural Computing & Applications. 37(17):10491-10505

Θεματικοί όροι: Non-intrusive Load Monitoring, Energy Efficiency, Deep Convolutional Neural Networks, Interpretability, Multi-target NILM models, data- och systemvetenskap, Computer and Systems Sciences

Περιγραφή αρχείου: print

Σύνδεσμος πρόσβασης: https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-237967
https://doi.org/10.1007/s00521-024-10552-0

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AIREM: AI-based Residential Electricity Monitoring System for Non-intrusive Monitoring

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Relation: 2025 IEEE Industrial Electronics and Applications Conference (IEACon)

A Hybrid Mamba-ECA-UNet Architecture for Multi-Task Non-Intrusive Load Monitoring

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Optimized Load Identification Using Total Harmonic Distortion and Artificial Bee Colony Algorithm

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NILM Scheme Enabled Wide Range of Electrical Load Identification Using SST and SVM Techniques

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Comparison of Long-Term Energy Measurements of Elevator Systems with ISO 25745

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GT-NILM: A Generative, Transferable Non-Intrusive Load Monitoring System Based on Conditional Diffusion Models and Convolutional Neural Networks

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Non-Intrusive Load Monitoring via Binary Integer Search and Transient Load Feature Integration

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Advancing Non-Intrusive Load Monitoring: Predicting Appliance-Level Power Consumption With Indirect Supervision

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Seasonal-Trend Transformer-based Diffusion Model for Non-Intrusive Load Monitoring

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Non-Intrusive Load Monitoring Missing Data Recovery via Nonlinear PID-Incorporated Latent Factorization of Tensors

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A Hybrid Supervised Deep Learning Approach for Non-intrusive Load Monitoring

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