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Updated: Jul 23, 2025

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基于可解释性的特征选择和性能预测,用于非侵入性负载监控

Rachel Stephen Mollel1, Lina Stankovic1, Vladimir Stankovic1

  • 1Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括

这项研究通过使用可解释的决策树和可解释性工具来增强非侵入性负载监控 (NILM). 这种方法提高了设备分类的准确性,并减少了智能电表能源数据的预测时间.

关键词:
尼尔姆 (NILM) 的意思是尼尔姆.决策树是一个决策树.可以解释性的解释性.多种分类的多重分类.

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