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An Extreme Learning Machine Approach to Effective Energy Disaggregation
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Modeling and predicting building's energy use with artificial neural networks: Methods and results
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Scoping the potential of monitoring and control technologies to reduce energy use in homes
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Unsupervised approach for load disaggregation with devices interactions
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The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
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Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overview
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A new approach for supervised power disaggregation by using a deep recurrent LSTM network
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Nonintrusive appliance load monitoring for smart homes: recent advances and future issues
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Extreme Learning Machines [Trends & Controversies]
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Disaggregation of home energy display data using probabilistic approach
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Power-Spectrum-Based Wavelet Transform for Nonintrusive Demand Monitoring and Load Identification
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PALDi: Online Load Disaggregation via Particle Filtering
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A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
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Unsupervised Disaggregation of Low Frequency Power Measurements
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ImageNet classification with deep convolutional neural networks
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Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation
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Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types
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Sequence-to-Point Learning With Neural Networks for Non-Intrusive Load Monitoring
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Embedded Fuzzy Logic Controller and Wireless Communication for Home Energy Management Systems
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An Extreme Learning Machine Approach to Effective Energy Disaggregation
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