Deep Recurrent Neural Network to Disaggregate Household Energy Consumption

Non-intrusive Load Monitoring (NILM) is a technique which accepts the total consumption in a house as an input and computes the estimated demand of individual appliances in that house. All NILM needs is one single meter to record the total power of a house. With NILM, the users can assess the inform...

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Detalles Bibliográficos
Autor: Linh, Viet Nguyen
Tipo de recurso: tesis de maestría
Fecha de publicación:2017
País:España
Institución:Universidad de Oviedo (UNIOVI)
Repositorio:RUO. Repositorio Institucional de la Universidad de Oviedo
Idioma:inglés
OAI Identifier:oai:digibuo.uniovi.es:10651/44555
Acceso en línea:http://hdl.handle.net/10651/44555
Access Level:acceso abierto
Palabra clave:Non-intrusive Load Monitoring
Energy Disaggregation
Recurrent Neural Network
Genetic Algorithm
Reference Energy Disaggregation Data Set
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spelling Deep Recurrent Neural Network to Disaggregate Household Energy ConsumptionLinh, Viet NguyenNon-intrusive Load MonitoringEnergy DisaggregationRecurrent Neural NetworkGenetic AlgorithmReference Energy Disaggregation Data SetNon-intrusive Load Monitoring (NILM) is a technique which accepts the total consumption in a house as an input and computes the estimated demand of individual appliances in that house. All NILM needs is one single meter to record the total power of a house. With NILM, the users can assess the information of appliances without the intrusion of measurement devices. Such information could help users adapt their energy-usage habit for better saving and facilitate the grid management. This motivates our effort to research the NILM topic, which focuses on disaggregation algorithm in this thesis. The main goals of this thesis are to build a web interface for the energy usage feedback, to enhance the disaggregation algorithms as well as to investigate the non-intrusive technique in fault study. The first part of this work reviews the NILM background and states whether NILM is helpful to alleviate the climate change. The second part describes various sources of dataset and selects one for visualizing in the web interface. This is the first attempt to build an online energy feedback platform for the end-users. Our third effort is to advance the concept for disaggregation algorithm, which is the deep recurrent neural network (DRNN). %The experiments were tested in two scenarios: the first was to test the learning models in a house seen during training and the other was to test those models in a house not involved during training. The results showed the better performance achieved by DRNN, compared with the classic optimization. DRNN has also done a fair job to test unseen appliances. Finally, the application of non-intrusive technique to fault study has been initially studied. Our discussion has found the feasibility of non-intrusive fault detection with satisfactory outcomes.This work was partially supported by the Spanish Ministry of Economy and Competitivity under Grant MINECO-17-ENE2016-77919-R (CONCIALIATOR Energy conversion technologies in resilient hybrid AC/DC distribution networks.Arboleya Arboleya, Pablo20172017-11-27master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesishttp://hdl.handle.net/10651/44555reponame:RUO. Repositorio Institucional de la Universidad de Oviedoinstname:Universidad de Oviedo (UNIOVI)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:digibuo.uniovi.es:10651/445552026-06-07T06:38:51Z
dc.title.none.fl_str_mv Deep Recurrent Neural Network to Disaggregate Household Energy Consumption
title Deep Recurrent Neural Network to Disaggregate Household Energy Consumption
spellingShingle Deep Recurrent Neural Network to Disaggregate Household Energy Consumption
Linh, Viet Nguyen
Non-intrusive Load Monitoring
Energy Disaggregation
Recurrent Neural Network
Genetic Algorithm
Reference Energy Disaggregation Data Set
title_short Deep Recurrent Neural Network to Disaggregate Household Energy Consumption
title_full Deep Recurrent Neural Network to Disaggregate Household Energy Consumption
title_fullStr Deep Recurrent Neural Network to Disaggregate Household Energy Consumption
title_full_unstemmed Deep Recurrent Neural Network to Disaggregate Household Energy Consumption
title_sort Deep Recurrent Neural Network to Disaggregate Household Energy Consumption
dc.creator.none.fl_str_mv Linh, Viet Nguyen
author Linh, Viet Nguyen
author_facet Linh, Viet Nguyen
author_role author
dc.contributor.none.fl_str_mv Arboleya Arboleya, Pablo
dc.subject.none.fl_str_mv Non-intrusive Load Monitoring
Energy Disaggregation
Recurrent Neural Network
Genetic Algorithm
Reference Energy Disaggregation Data Set
topic Non-intrusive Load Monitoring
Energy Disaggregation
Recurrent Neural Network
Genetic Algorithm
Reference Energy Disaggregation Data Set
description Non-intrusive Load Monitoring (NILM) is a technique which accepts the total consumption in a house as an input and computes the estimated demand of individual appliances in that house. All NILM needs is one single meter to record the total power of a house. With NILM, the users can assess the information of appliances without the intrusion of measurement devices. Such information could help users adapt their energy-usage habit for better saving and facilitate the grid management. This motivates our effort to research the NILM topic, which focuses on disaggregation algorithm in this thesis. The main goals of this thesis are to build a web interface for the energy usage feedback, to enhance the disaggregation algorithms as well as to investigate the non-intrusive technique in fault study. The first part of this work reviews the NILM background and states whether NILM is helpful to alleviate the climate change. The second part describes various sources of dataset and selects one for visualizing in the web interface. This is the first attempt to build an online energy feedback platform for the end-users. Our third effort is to advance the concept for disaggregation algorithm, which is the deep recurrent neural network (DRNN). %The experiments were tested in two scenarios: the first was to test the learning models in a house seen during training and the other was to test those models in a house not involved during training. The results showed the better performance achieved by DRNN, compared with the classic optimization. DRNN has also done a fair job to test unseen appliances. Finally, the application of non-intrusive technique to fault study has been initially studied. Our discussion has found the feasibility of non-intrusive fault detection with satisfactory outcomes.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-11-27
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10651/44555
url http://hdl.handle.net/10651/44555
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:RUO. Repositorio Institucional de la Universidad de Oviedo
instname:Universidad de Oviedo (UNIOVI)
instname_str Universidad de Oviedo (UNIOVI)
reponame_str RUO. Repositorio Institucional de la Universidad de Oviedo
collection RUO. Repositorio Institucional de la Universidad de Oviedo
repository.name.fl_str_mv
repository.mail.fl_str_mv
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