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''This page is about the applications of machine learning (ML) in the context of non-intrusive load monitoring (NILM). For an overview of NILM more generally, please see the [https://en.wikipedia.org/wiki/Nonintrusive_load_monitoring Wikipedia page] on this topic.''
A better understanding of the own energy consumption can lead to better energy efficiency by changing one's behavior or exchanging inefficient devices. ML can help to disaggregate a household's smart meter data and attribute energy consumption to individual devices for increased transparency. ▼
▲A better understanding of the own energy consumption can lead to better energy efficiency by changing one's behavior or exchanging inefficient devices.
==Background Readings==
==Community==
==Libraries and Tools==
*'''NILMTK''': An Open source NILM toolkit in Python that provides wrappers to popular datasets, has implemented several benchmark algorithms and provides standard interfaces for benchmarking NILM algorithms, available [https://nilmtk.github.io/ here].
==Data==
==Future Directions==
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