非侵入式负载监控(NILM):无监督机器学习和特征融合:私人和工业应用的能源管理

Timo Bernard, Martin H. Verbunt, G. V. Bögel, Thorsten Wellmann
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引用次数: 14

摘要

节约能源是向清洁能源转型的重要组成部分。研究表明,考虑整体负荷分布不足以确定显著的节约潜力-智能电表的情况也是如此。非侵入式负载监控能够以经济有效的方式对设备特定的消耗进行分解。我们的工作重点是融合低、中、高频特征,以提高分解性能。此外,我们建议的方法包括一种无监督机器学习技术,该技术可以实现新颖性检测、小训练阶段和实时处理。最后,我们对家庭和工业数据集的算法进行了评价。
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Non-Intrusive Load Monitoring (NILM): Unsupervised Machine Learning and Feature Fusion : Energy Management for Private and Industrial Applications
Energy savings are an important building block for the clean energy transition. Studies show that the consideration of overall load profiles is not sufficient to identify significant saving potentials -as is the case with smart meters. Nonintrusive Load Monitoring enables a device specific consumption disaggregation in a cost effective way. Our work focuses on the fusion of low, mid and high frequency features which can enhance the disaggregation performance. Furthermore our suggested approach consists of an unsupervised machine learning technique which enables novelty detection, a small training phase and live processing. We conclude this paper with the algorithm evaluation on household and industrial datasets.
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