Non-Intrusive Load Monitoring
14 papers with code • 0 benchmarks • 1 datasets
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Latest papers
On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring
To assess the performance of load disaggregation algorithms it is common practise to train a candidate algorithm on data from one or multiple households and subsequently apply cross-validation by evaluating the classification and energy estimation performance on unseen portions of the dataset derived from the same households.
Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
inesylla/energy-disaggregation-DL
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Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances.
Transfer Learning for Non-Intrusive Load Monitoring
It is not clear if the method could be generalised or transferred to different domains, e. g., the test data were drawn from a different country comparing to the training data.
Wavenilm: A causal neural network for power disaggregation from the complex power signal
Non-intrusive load monitoring (NILM) helps meet energy conservation goals by estimating individual appliance power usage from a single aggregate measurement.
