Using explainability tools to inform NILM algorithm performance
Author:
Affiliation:
1. University of Strathclyde, Glasgow, UK
Funder
Commonwealth Scholarship Commission
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3563357.3566148
Reference15 articles.
1. NILM applications: Literature review of learning approaches, recent developments and challenges
2. A Survey on the Explainability of Supervised Machine Learning
3. Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation
4. Non-Intrusive Load Disaggregation Using Graph Signal Processing
5. Review on Deep Neural Networks Applied to Low-Frequency NILM
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