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https://hdl.handle.net/20.500.13091/3155
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DC Field | Value | Language |
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dc.contributor.author | Çimen, Halil | - |
dc.contributor.author | Wu, Ying | - |
dc.contributor.author | Wu, Yanpeng | - |
dc.contributor.author | Terriche, Yacine | - |
dc.contributor.author | Vasquez, Juan C. | - |
dc.contributor.author | Guerrero, Josep M. | - |
dc.date.accessioned | 2022-11-28T16:54:42Z | - |
dc.date.available | 2022-11-28T16:54:42Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 1551-3203 | - |
dc.identifier.issn | 1941-0050 | - |
dc.identifier.uri | https://doi.org/10.1109/TII.2022.3150334 | - |
dc.identifier.uri | https://doi.org/10.1109/TII.2022.3150334 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/3155 | - |
dc.description.abstract | Energy disaggregation is the process of disaggregating a household's total energy consumption into its appliance-level components. One of the limitations of energy disaggregation is its generalization capacity, which can be defined as the ability of the model to analyze new households. In this article, a new energy disaggregation approach based on adversarial autoencoder (AAE) is proposed to create a generative model and enhance the generalization capacity. The proposed method has a probabilistic structure to handle uncertainties in the unseen data. By transforming the latent space from a deterministic structure to a Gaussian prior distribution, AAEs decoder transforms into a generative model. The proposed approach is validated through experimental tests using two different datasets. The experimental results exhibit a 55% MAE performance increase compared to deterministic models and 7% compared to probabilistic models. In addition, considering the predictions made when the appliances are on, the AAE improves the performance by 16% for UKDALE and 36% for REDD dataset compared to the state-of-art models. Moreover, the online analysis performance of AAE is examined in detail, and the disadvantages of instant predictions and the possible solutions are extensively discussed. | en_US |
dc.description.sponsorship | VILLUM FONDEN [25920]; Center for Research on Microgrids; AAU Talent Project-The Energy Internet-Integrating Internet of Things into the Smart Grid [771116] | en_US |
dc.description.sponsorship | This work was supported in part by the VILLUM FONDEN under the VILLUM Investigator under Grant 25920, in part by the Center for Research on Microgrids, and in part by the AAU Talent Project-The Energy Internet-Integrating Internet of Things into the Smart Grid under Grant 771116. Paper no. TII-21-2563. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Ieee-Inst Electrical Electronics Engineers Inc | en_US |
dc.relation.ispartof | IEEE Transactions On Industrial Informatics | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Hidden Markov models | en_US |
dc.subject | Training | en_US |
dc.subject | Probabilistic logic | en_US |
dc.subject | Analytical models | en_US |
dc.subject | Generative adversarial networks | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Data models | en_US |
dc.subject | Adversarial autoencoder (AAE) | en_US |
dc.subject | deep learning | en_US |
dc.subject | energy disaggregation | en_US |
dc.subject | generative adversarial networks | en_US |
dc.subject | nonintrusive load monitoring (NILM) | en_US |
dc.subject | online energy disaggregation | en_US |
dc.subject | probabilistic energy disaggregation | en_US |
dc.subject | residential energy disaggregation | en_US |
dc.subject | Load Disaggregation | en_US |
dc.title | Deep Learning-Based Probabilistic Autoencoder for Residential Energy Disaggregation: An Adversarial Approach | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/TII.2022.3150334 | - |
dc.identifier.scopus | 2-s2.0-85124765333 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.authorid | Wu, Ying/0000-0001-5602-3015 | - |
dc.authorid | Vasquez, Juan C./0000-0001-6332-385X | - |
dc.authorid | CIMEN, HALIL/0000-0003-0104-3005 | - |
dc.authorwosid | Vasquez, Juan C./J-2247-2014 | - |
dc.identifier.volume | 18 | en_US |
dc.identifier.issue | 12 | en_US |
dc.identifier.startpage | 8399 | en_US |
dc.identifier.endpage | 8408 | en_US |
dc.identifier.wos | WOS:000862429800007 | en_US |
dc.institutionauthor | Çimen, Halil | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57205614115 | - |
dc.authorscopusid | 55556950900 | - |
dc.authorscopusid | 56067425600 | - |
dc.authorscopusid | 56470769200 | - |
dc.authorscopusid | 57203104097 | - |
dc.authorscopusid | 57226523614 | - |
item.grantfulltext | embargo_20300101 | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Article | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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Deep_Learning-Based_Probabilistic_Autoencoder_for_Residential_Energy_Disaggregation_An_Adversarial_Approach.pdf Until 2030-01-01 | 2.99 MB | Adobe PDF | View/Open Request a copy |
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