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Found 2 papers, 0 papers with code
Exploring Bayesian Surprise to Prevent Overfitting and to Predict Model Performance in Non-Intrusive Load Monitoring
We compare the performance of several benchmark NILM algorithms supported by NILMTK, in order to establish a useful threshold on the two combined measures of surprise.
PowerGAN: Synthesizing Appliance Power Signatures Using Generative Adversarial Networks
Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into home appliance electricity consumption using only the building's smart meter.
Generative Adversarial Network
Non-Intrusive Load Monitoring
