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https://hdl.handle.net/20.500.13091/377
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Çimen, Halil | - |
dc.contributor.author | Çetinkaya, Nurettin | - |
dc.contributor.author | Vasquez, Juan C. | - |
dc.contributor.author | Guerrero, Josep M. | - |
dc.date.accessioned | 2021-12-13T10:24:07Z | - |
dc.date.available | 2021-12-13T10:24:07Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1949-3053 | - |
dc.identifier.issn | 1949-3061 | - |
dc.identifier.uri | https://doi.org/10.1109/TSG.2020.3027491 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/377 | - |
dc.description.abstract | Non-intrusive load monitoring (NILM) enables to understand the appliance-level behavior of the consumers by using only smart meter data, and it mitigates the requirements such as high-cost sensors, maintenance/update and provides a cost-effective solution. This article presents an efficient NILM-based energy management system (EMS) for residential microgrids. Firstly, smart meter data are analyzed with a multi-task deep neural network-based approach and the appliance-level information of the consumers is extracted. Both consumption and operating status of the appliances are obtained. Afterward, the energy consumption behaviors of the end-users are analyzed using these data. Accordingly, average power consumption, operation cycles, preferred usage periods, and daily usage frequency of the appliances were obtained with an average accuracy of more than 90%. The obtained results were integrated into an EMS to create an efficient and user-centered microgrid operation. The developed model not only provided the optimum dispatch of distributed generation plants in the microgrid but also scheduled the controllable loads taking into account customers' satisfaction. It was demonstrated with the help of simulation that the proposed NILM-based EMS model improves the operation cost/customer satisfaction ratio between 45% and 65% compared to a traditional EMS. | en_US |
dc.description.sponsorship | Scientific and Technological Research Council of Turkey (TUBITAK) BIDEB-2214 International Doctoral Research Fellowship ProgrammeTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK); VILLUM FONDEN [25920]; Aalborg University 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 Scientific and Technological Research Council of Turkey (TUBITAK) BIDEB-2214 International Doctoral Research Fellowship Programme; in part by the VILLUM FONDEN under the VILLUM Investigator under Grant 25920 [Center for Research on Microgrids (CROM)]; and in part by the Aalborg University Talent Project-The Energy Internet-Integrating Internet of Things Into the Smart Grid under Grant 771116. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | en_US |
dc.relation.ispartof | IEEE TRANSACTIONS ON SMART GRID | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Home appliances | en_US |
dc.subject | Hidden Markov models | en_US |
dc.subject | Energy management | en_US |
dc.subject | Monitoring | en_US |
dc.subject | Smart meters | en_US |
dc.subject | Energy consumption | en_US |
dc.subject | Microgrids | en_US |
dc.subject | Non-intrusive load monitoring | en_US |
dc.subject | microgrid | en_US |
dc.subject | energy management | en_US |
dc.subject | recurrent neural network | en_US |
dc.subject | deep learning | en_US |
dc.title | A Microgrid Energy Management System Based on Non-Intrusive Load Monitoring via Multitask Learning | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/TSG.2020.3027491 | - |
dc.identifier.scopus | 2-s2.0-85100324779 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.authorid | Vasquez, Juan C./0000-0001-6332-385X | - |
dc.authorwosid | Vasquez, Juan C./J-2247-2014 | - |
dc.authorwosid | Guerrero, Josep/D-5519-2014 | - |
dc.identifier.volume | 12 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.startpage | 977 | en_US |
dc.identifier.endpage | 987 | en_US |
dc.identifier.wos | WOS:000623420700007 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57205614115 | - |
dc.authorscopusid | 10739795700 | - |
dc.authorscopusid | 57203104097 | - |
dc.authorscopusid | 35588010400 | - |
item.grantfulltext | embargo_20300101 | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
item.openairetype | Article | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
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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A_Microgrid_Energy_Management_System_Based_on_Non-Intrusive_Load_Monitoring_via_Multitask_Learning.pdf Until 2030-01-01 | 2.05 MB | Adobe PDF | View/Open Request a copy |
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