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https://dspace.univ-ouargla.dz/jspui/handle/123456789/29023
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DC Field | Value | Language |
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dc.contributor.advisor | Mohamed, redouane Kafi | - |
dc.contributor.advisor | Benglia, Elhacen | - |
dc.contributor.author | Isamil, Laggoun, | - |
dc.contributor.author | Hosseyn, Bougherara | - |
dc.contributor.author | Benmesbah, Mohamed Amine | - |
dc.date.accessioned | 2022-05-16T15:00:53Z | - |
dc.date.available | 2022-05-16T15:00:53Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://dspace.univ-ouargla.dz/jspui/handle/123456789/29023 | - |
dc.description.abstract | Non-intrusive load monitoring (NILM) is an energy management solution, it gives us statistics of energy consumption specific to each device, with NILM we have optimal energy management, or the other solutions offering Intrusive Load Monitoring (ILM) requires a smart plug installation resulting in additional hardware cost and installation complexity, NILM only needs one metering point, as it can discern devices from aggregate data acquired from a single measuring point. In this work we will show the principles of NILM and the methods and techniques used for the detection of disaggregated energy and show the algorithms used for. | en_US |
dc.language.iso | en | en_US |
dc.publisher | University Kasdi Merbah Ouargla | en_US |
dc.subject | Non-Intrusive Load Monitoring (NILM) | en_US |
dc.subject | Intrusive Load Monitoring (ILM), | en_US |
dc.subject | Machine learning, | en_US |
dc.subject | Deep learning | en_US |
dc.title | Non-Intrusive Load Monitoring (NILM) for Energy Consumption in Householder. | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Département d'Electronique et des Télécommunications - Master |
Files in This Item:
File | Description | Size | Format | |
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Laggoun-Bougherara-Benmesbah.pdf | Automatic, Automatic and Systems | 1,65 MB | Adobe PDF | View/Open |
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