Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model

Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models...
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Autor*in:	Ce Peng [verfasserIn] Guoying Lin [verfasserIn] Shaopeng Zhai [verfasserIn] Yi Ding [verfasserIn] Guangyu He [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	NILM deep learning deep user model deep appliance group model user behavior

Übergeordnetes Werk:	In: Energies - MDPI AG, 2008, 13(2020), 21, p 5629
Übergeordnetes Werk:	volume:13 ; year:2020 ; number:21, p 5629

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/en13215629

Katalog-ID:	DOAJ022781811

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allfields_unstemmed	10.3390/en13215629 doi (DE-627)DOAJ022781811 (DE-599)DOAJ115faa26d0fc4715999097a88a1edf2a DE-627 ger DE-627 rakwb eng Ce Peng verfasserin aut Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models perform more efficiently and preserve longer-term information, while super-state helps the model to learn correlations between appliances. The first model is a deep user model that learns user appliances usage patterns to predict the next appliance usage behavior based on past behaviors by capturing the dynamics of user behaviors history and appliances usage habits. The second model is a deep appliance group model that learns the characteristics of appliances with temporal and electrical information. These two models are then fused to perform NILM. The case study based on REFIT datasets demonstrates that the proposed NILM method outperforms two state-of-the-art benchmark methods. NILM deep learning deep user model deep appliance group model user behavior Technology T Guoying Lin verfasserin aut Shaopeng Zhai verfasserin aut Yi Ding verfasserin aut Guangyu He verfasserin aut In Energies MDPI AG, 2008 13(2020), 21, p 5629 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:13 year:2020 number:21, p 5629 https://doi.org/10.3390/en13215629 kostenfrei https://doaj.org/article/115faa26d0fc4715999097a88a1edf2a kostenfrei https://www.mdpi.com/1996-1073/13/21/5629 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 21, p 5629
allfieldsGer	10.3390/en13215629 doi (DE-627)DOAJ022781811 (DE-599)DOAJ115faa26d0fc4715999097a88a1edf2a DE-627 ger DE-627 rakwb eng Ce Peng verfasserin aut Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models perform more efficiently and preserve longer-term information, while super-state helps the model to learn correlations between appliances. The first model is a deep user model that learns user appliances usage patterns to predict the next appliance usage behavior based on past behaviors by capturing the dynamics of user behaviors history and appliances usage habits. The second model is a deep appliance group model that learns the characteristics of appliances with temporal and electrical information. These two models are then fused to perform NILM. The case study based on REFIT datasets demonstrates that the proposed NILM method outperforms two state-of-the-art benchmark methods. NILM deep learning deep user model deep appliance group model user behavior Technology T Guoying Lin verfasserin aut Shaopeng Zhai verfasserin aut Yi Ding verfasserin aut Guangyu He verfasserin aut In Energies MDPI AG, 2008 13(2020), 21, p 5629 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:13 year:2020 number:21, p 5629 https://doi.org/10.3390/en13215629 kostenfrei https://doaj.org/article/115faa26d0fc4715999097a88a1edf2a kostenfrei https://www.mdpi.com/1996-1073/13/21/5629 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 21, p 5629
allfieldsSound	10.3390/en13215629 doi (DE-627)DOAJ022781811 (DE-599)DOAJ115faa26d0fc4715999097a88a1edf2a DE-627 ger DE-627 rakwb eng Ce Peng verfasserin aut Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models perform more efficiently and preserve longer-term information, while super-state helps the model to learn correlations between appliances. The first model is a deep user model that learns user appliances usage patterns to predict the next appliance usage behavior based on past behaviors by capturing the dynamics of user behaviors history and appliances usage habits. The second model is a deep appliance group model that learns the characteristics of appliances with temporal and electrical information. These two models are then fused to perform NILM. The case study based on REFIT datasets demonstrates that the proposed NILM method outperforms two state-of-the-art benchmark methods. NILM deep learning deep user model deep appliance group model user behavior Technology T Guoying Lin verfasserin aut Shaopeng Zhai verfasserin aut Yi Ding verfasserin aut Guangyu He verfasserin aut In Energies MDPI AG, 2008 13(2020), 21, p 5629 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:13 year:2020 number:21, p 5629 https://doi.org/10.3390/en13215629 kostenfrei https://doaj.org/article/115faa26d0fc4715999097a88a1edf2a kostenfrei https://www.mdpi.com/1996-1073/13/21/5629 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 21, p 5629
language	English
source	In Energies 13(2020), 21, p 5629 volume:13 year:2020 number:21, p 5629
sourceStr	In Energies 13(2020), 21, p 5629 volume:13 year:2020 number:21, p 5629
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author	Ce Peng
spellingShingle	Ce Peng misc NILM misc deep learning misc deep user model misc deep appliance group model misc user behavior misc Technology misc T Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model
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title	Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model
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title_sort	non-intrusive load monitoring via deep learning based user model and appliance group model
title_auth	Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model
abstract	Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models perform more efficiently and preserve longer-term information, while super-state helps the model to learn correlations between appliances. The first model is a deep user model that learns user appliances usage patterns to predict the next appliance usage behavior based on past behaviors by capturing the dynamics of user behaviors history and appliances usage habits. The second model is a deep appliance group model that learns the characteristics of appliances with temporal and electrical information. These two models are then fused to perform NILM. The case study based on REFIT datasets demonstrates that the proposed NILM method outperforms two state-of-the-art benchmark methods.
abstractGer	Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models perform more efficiently and preserve longer-term information, while super-state helps the model to learn correlations between appliances. The first model is a deep user model that learns user appliances usage patterns to predict the next appliance usage behavior based on past behaviors by capturing the dynamics of user behaviors history and appliances usage habits. The second model is a deep appliance group model that learns the characteristics of appliances with temporal and electrical information. These two models are then fused to perform NILM. The case study based on REFIT datasets demonstrates that the proposed NILM method outperforms two state-of-the-art benchmark methods.
abstract_unstemmed	Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models perform more efficiently and preserve longer-term information, while super-state helps the model to learn correlations between appliances. The first model is a deep user model that learns user appliances usage patterns to predict the next appliance usage behavior based on past behaviors by capturing the dynamics of user behaviors history and appliances usage habits. The second model is a deep appliance group model that learns the characteristics of appliances with temporal and electrical information. These two models are then fused to perform NILM. The case study based on REFIT datasets demonstrates that the proposed NILM method outperforms two state-of-the-art benchmark methods.
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title_short	Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model
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