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https://hdl.handle.net/20.500.13091/2429
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Çimen, Halil | - |
dc.contributor.author | Palacios-Garcia, Emilio J. | - |
dc.contributor.author | Çetinkaya, Nurettin | - |
dc.contributor.author | Kolbak, Morten | - |
dc.contributor.author | Sciume, Giuseppe | - |
dc.contributor.author | Vasquez, Juan C. | - |
dc.contributor.author | Guerrero, Josep M. | - |
dc.date.accessioned | 2022-05-23T20:22:43Z | - |
dc.date.available | 2022-05-23T20:22:43Z | - |
dc.date.issued | 2020 | - |
dc.identifier.isbn | 978-1-7281-5200-4 | - |
dc.identifier.issn | 2158-8481 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/2429 | - |
dc.description | 20th IEEE Mediterranean Eletrotechnical Conference (IEEE MELECON) -- JUN 15-18, 2020 -- ELECTR NETWORK | en_US |
dc.description.abstract | Appliance Load Monitoring is a technique used to monitor devices existing in homes, industry or naval vessels. Acquisition of device-level data can provide great benefits in many areas such as energy management, demand response, and load forecasting. However, the monitoring process is often provided with a costly installation, as it requires a large number of sensors and a data center. Non-Intrusive Load Monitoring (NILM) is an alternative and cost-efficient load monitoring solution. Simply put, NILM is the process of obtaining device-level data by analyzing the aggregated data read from the main meter that measures the electricity consumption of the whole house. Before NILM analysis is performed, the load patterns of the appliances are usually modeled individually. In general, one model for each appliance is modeled even if the appliance has more than one operating program such as washing machine and oven. Therefore, when the appliance operates in other programs, the accuracy of NILM analysis decreases. In this paper, an appliance-based NILM analysis has been made considering the appliances having multiple operating programs. In order to increase the accuracy of NILM analysis, several deep learning methods, which are the most important data-driven technique of recent times, are used. Developed models were tested in IoT Microgrid Laboratory environment. | en_US |
dc.description.sponsorship | IEEE, IEEE Reg 8, IEEE Italy Sect, Univ Palermo, IEEE Ind Applicat Soc, ABB, IEEE Entrepreneurship, MDPI, Sensors Journal | en_US |
dc.description.sponsorship | Scientific and Technological Research Council of Turkey (TUBITAK) International Doctoral Research Fellowship Programme [BIDEB-2214] | en_US |
dc.description.sponsorship | This work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) BIDEB-2214 International Doctoral Research Fellowship Programme. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Ieee | en_US |
dc.relation.ispartof | 20th Ieee Mediterranean Eletrotechnical Conference (Ieee Melecon 2020) | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | load monitoring | en_US |
dc.subject | deep learning | en_US |
dc.subject | energy disaggregation | en_US |
dc.subject | demand response | en_US |
dc.subject | energy management | en_US |
dc.title | Generalization Capacity Analysis of Non-Intrusive Load Monitoring using Deep Learning | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.1109/MELECON48756.2020.9140688 | - |
dc.identifier.scopus | 2-s2.0-85089275174 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.authorid | Palacios-Garcia, Emilio Jose/0000-0003-2703-6532 | - |
dc.authorid | Kolbaek, Morten/0000-0002-2561-4960 | - |
dc.authorid | Guerrero, Josep/0000-0001-5236-4592 | - |
dc.authorid | Vasquez, Juan C./0000-0001-6332-385X | - |
dc.authorid | , Giuseppe/0000-0002-2384-5706 | - |
dc.authorwosid | Palacios-Garcia, Emilio Jose/K-9567-2015 | - |
dc.authorwosid | Guerrero, Josep/D-5519-2014 | - |
dc.authorwosid | Vasquez, Juan C./J-2247-2014 | - |
dc.identifier.startpage | 216 | en_US |
dc.identifier.endpage | 220 | en_US |
dc.identifier.wos | WOS:000783754700040 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
item.languageiso639-1 | en | - |
item.grantfulltext | embargo_20300101 | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
item.openairetype | Conference Object | - |
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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Generalization_Capacity_Analysis_of_Non-_Intrusive_Load_Monitoring_using_Deep_Learning.pdf Until 2030-01-01 | 1.49 MB | Adobe PDF | View/Open Request a copy |
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