Deep Learning Applications in non-intrusive load monitoring

Bibliographic Details
Main Author: Gaimes, Yousra
Publication Date: 2022
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10400.1/18761
Summary: Within the frame of the project Non-Intrusive Load Monitoring for Intelligent Home Energy Management Systems, this work will present a deep learning application in non-intrusive load monitoring on a case study in a residential home in in Gambelas, Faro in the Algarve region south of Portugal. This work has for a goal to detect type 2 appliances in different houses. For the sake of this study, two models will be trained: - Convolutional Neural Network - Long Short-term Memory Recurrent Neural Network on three datasets: - UKDale - REDD - Data from the Portuguese private residential house from the project NILM for IHEMS.
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spelling Deep Learning Applications in non-intrusive load monitoringNILMCNNLSTMUKDaleREDDNILM for IHEMSWithin the frame of the project Non-Intrusive Load Monitoring for Intelligent Home Energy Management Systems, this work will present a deep learning application in non-intrusive load monitoring on a case study in a residential home in in Gambelas, Faro in the Algarve region south of Portugal. This work has for a goal to detect type 2 appliances in different houses. For the sake of this study, two models will be trained: - Convolutional Neural Network - Long Short-term Memory Recurrent Neural Network on three datasets: - UKDale - REDD - Data from the Portuguese private residential house from the project NILM for IHEMS.Ruano, A. E.SapientiaGaimes, Yousra2023-01-09T11:08:21Z2022-02-172022-02-17T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.1/18761urn:tid:203012046enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-18T17:39:13Zoai:sapientia.ualg.pt:10400.1/18761Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T20:30:28.766806Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Deep Learning Applications in non-intrusive load monitoring
title Deep Learning Applications in non-intrusive load monitoring
spellingShingle Deep Learning Applications in non-intrusive load monitoring
Gaimes, Yousra
NILM
CNN
LSTM
UKDale
REDD
NILM for IHEMS
title_short Deep Learning Applications in non-intrusive load monitoring
title_full Deep Learning Applications in non-intrusive load monitoring
title_fullStr Deep Learning Applications in non-intrusive load monitoring
title_full_unstemmed Deep Learning Applications in non-intrusive load monitoring
title_sort Deep Learning Applications in non-intrusive load monitoring
author Gaimes, Yousra
author_facet Gaimes, Yousra
author_role author
dc.contributor.none.fl_str_mv Ruano, A. E.
Sapientia
dc.contributor.author.fl_str_mv Gaimes, Yousra
dc.subject.por.fl_str_mv NILM
CNN
LSTM
UKDale
REDD
NILM for IHEMS
topic NILM
CNN
LSTM
UKDale
REDD
NILM for IHEMS
description Within the frame of the project Non-Intrusive Load Monitoring for Intelligent Home Energy Management Systems, this work will present a deep learning application in non-intrusive load monitoring on a case study in a residential home in in Gambelas, Faro in the Algarve region south of Portugal. This work has for a goal to detect type 2 appliances in different houses. For the sake of this study, two models will be trained: - Convolutional Neural Network - Long Short-term Memory Recurrent Neural Network on three datasets: - UKDale - REDD - Data from the Portuguese private residential house from the project NILM for IHEMS.
publishDate 2022
dc.date.none.fl_str_mv 2022-02-17
2022-02-17T00:00:00Z
2023-01-09T11:08:21Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.1/18761
urn:tid:203012046
url http://hdl.handle.net/10400.1/18761
identifier_str_mv urn:tid:203012046
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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