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A novel DNN-HMM-based approach for extracting single loads from aggregate power signals

https://doi.org/10.1109/icassp.2016.7472104

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Nonintrusive appliance load monitoring

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Multi-channel source separation by factorial HMMs

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Robust adaptive event detection in non-intrusive load monitoring for energy aware smart facilities

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Deep NMF for speech separation

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Representation models in single channel source separation

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Genetic algorithm for pattern detection in NIALM systems

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A hidden Markov model based procedure for identifying household electric loads

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Circuit-Level Load Monitoring for Household Energy Management

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Disaggregated End-Use Energy Sensing for the Smart Grid

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Nonintrusive appliance load monitoring based on integer programming

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Nonintrusive appliance load monitoring: Review and outlook

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Nonintrusive Appliance Load Monitoring based on Integer Programming

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Theano: A CPU and GPU Math Compiler in Python

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Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey

inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot

Scikit-learn: Machine Learning in Python

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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