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dc.contributor.authorÇimen, Halil-
dc.contributor.authorPalacios-Garcia, Emilio J.-
dc.contributor.authorÇetinkaya, Nurettin-
dc.contributor.authorKolbak, Morten-
dc.contributor.authorSciume, Giuseppe-
dc.contributor.authorVasquez, Juan C.-
dc.contributor.authorGuerrero, Josep M.-
dc.description20th IEEE Mediterranean Eletrotechnical Conference (IEEE MELECON) -- JUN 15-18, 2020 -- ELECTR NETWORKen_US
dc.description.abstractAppliance 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.sponsorshipIEEE, IEEE Reg 8, IEEE Italy Sect, Univ Palermo, IEEE Ind Applicat Soc, ABB, IEEE Entrepreneurship, MDPI, Sensors Journalen_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) International Doctoral Research Fellowship Programme [BIDEB-2214]en_US
dc.description.sponsorshipThis work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) BIDEB-2214 International Doctoral Research Fellowship Programme.en_US
dc.relation.ispartof20th Ieee Mediterranean Eletrotechnical Conference (Ieee Melecon 2020)en_US
dc.subjectload monitoringen_US
dc.subjectdeep learningen_US
dc.subjectenergy disaggregationen_US
dc.subjectdemand responseen_US
dc.subjectenergy managementen_US
dc.titleGeneralization Capacity Analysis of Non-Intrusive Load Monitoring using Deep Learningen_US
dc.typeConference Objecten_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.authoridPalacios-Garcia, Emilio Jose/0000-0003-2703-6532-
dc.authoridKolbaek, Morten/0000-0002-2561-4960-
dc.authoridGuerrero, Josep/0000-0001-5236-4592-
dc.authoridVasquez, Juan C./0000-0001-6332-385X-
dc.authorid, Giuseppe/0000-0002-2384-5706-
dc.authorwosidPalacios-Garcia, Emilio Jose/K-9567-2015-
dc.authorwosidGuerrero, Josep/D-5519-2014-
dc.authorwosidVasquez, Juan C./J-2247-2014-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.fulltextWith Fulltext-
item.openairetypeConference Object-
item.openairecristype 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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