Comparison and Adaptation of Two Strategies for Anomaly Detection in Load Profiles Based on Methods from the Fields of Machine Learning and Statistics

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ISSN Print: 2169-2637
ISSN Online: 2169-2645
www.scirp.org/journal/ojee
E-mail: ojee@scirp.org

"Comparison and Adaptation of Two Strategies for Anomaly Detection in Load Profiles Based on Methods from the Fields of Machine Learning and Statistics"
written by Patrick Krawiec, Mark Junge, Jens Hesselbach,
published by Open Journal of Energy Efficiency, Vol.10 No.2, 2021

has been cited by the following article(s):

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[2]	Load configuration impact on energy community and distribution grid: Quantifying costs, emissions and grid exchange Applied Energy, 2024 DOI:10.1016/j.apenergy.2024.123060

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[6]	Anomaly Detection of Metallurgical Energy Data Based on iForest-AE Applied Sciences, 2022 DOI:10.3390/app12199977

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