Non-Intrusive Load Monitoring
14 papers with code • 0 benchmarks • 1 datasets
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Most implemented papers
Learning Task-Aware Energy Disaggregation: a Federated Approach
ruohliuq/fedmeta
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We consider the problem of learning the energy disaggregation signals for residential load data.
Challenges in Gaussian Processes for Non Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) or energy disaggregation aims to break down total household energy consumption into constituent appliances.
Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances
We also show a 12 percentage point performance advantage of the proposed DL based model over a random forest model and observe performance degradation with the increase of the number of devices in the household, namely with each additional 5 devices, the average performance degrades by approximately 7 percentage points.
MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data
jxiong22/matnilm
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Non-intrusive load monitoring (NILM) identifies the status and power consumption of various household appliances by disaggregating the total power usage signal of an entire house.
