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Found 4 papers, 2 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.
Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation
In this paper, we draw attention to comparability in NILM with a focus on highlighting the considerable differences amongst common energy datasets used to test the performance of algorithms.
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.
Non-Intrusive Load Monitoring: A Review and Outlook
With the roll-out of smart meters the importance of effective non-intrusive load monitoring (NILM) techniques has risen rapidly.
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