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Exploring Bayesian Surprise to Prevent Overfitting and to Predict Model Performance in Non-Intrusive Load Monitoring
Non-Intrusive Load Monitoring (NILM) is a field of research focused on s...
09/16/2020 ∙ by Richard Jones, et al. ∙
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Investigating the Performance Gap between Testing on Real and Denoised Aggregates in Non-Intrusive Load Monitoring
Prudent and meaningful performance evaluation of algorithms is essential...
08/22/2020 ∙ by Christoph Klemenjak, et al. ∙
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Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring
The need for reducing our energy consumption footprint and the increasin...
02/13/2020 ∙ by Hafsa Bousbiat, et al. ∙
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Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation
Non-Intrusive Load Monitoring (NILM) comprises of a set of techniques th...
01/20/2020 ∙ by Christoph Klemenjak, et al. ∙
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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...
12/12/2019 ∙ by Christoph Klemenjak, et al. ∙
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Non-Intrusive Load Monitoring: A Review and Outlook
With the roll-out of smart meters the importance of effective non-intrus...
10/04/2016 ∙ by Christoph Klemenjak, et al. ∙
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