Difference between revisions of "Non-Intrusive Load Monitoring"

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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. MLMachine Learning can be applied for the Nonintrusive load monitoring (NILM) task, i.e., can help to disaggregate a household's smart meter data and attribute energy consumption to individual devices for increased transparency.
 
==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==