PI-controlled ANN-based Energy Consumption Forecasting for Smart Grids

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dc.authoridTuna, Gurkan/0000-0002-6466-4696
dc.authoridKogias, Dimitrios/0000-0001-8985-6136
dc.authorwosidTuna, Gurkan/AAG-4412-2019
dc.authorwosidKogias, Dimitrios/AAP-7715-2021
dc.contributor.authorGezer, Gulsum
dc.contributor.authorTuna, Gurkan
dc.contributor.authorKogias, Dimitris
dc.contributor.authorGulez, Kayhan
dc.contributor.authorGungor, V. Cagri
dc.date.accessioned2024-06-12T11:14:16Z
dc.date.available2024-06-12T11:14:16Z
dc.date.issued2015
dc.departmentTrakya Üniversitesien_US
dc.description12th International Conference on Informatics in Control Automation and Robotics (ICINCO) -- JUL 21-23, 2015 -- Alsace, FRANCEen_US
dc.description.abstractAlthough Smart Grid (SG) transformation brings many advantages to electric utilities, the longstanding challenge for all them is to supply electricity at the lowest cost. In addition, currently, the electric utilities must comply with new expectations for their operations, and address new challenges such as energy efficiency regulations and guidelines, possibility of economic recessions, volatility of fuel prices, new user profiles and demands of regulators. In order to meet all these emerging economic and regulatory realities, the electric utilities operating SGs must be able to determine and meet load, implement new technologies that can effect energy sales and interact with their customers for their purchases of electricity. In this respect, load forecasting which has traditionally been done mostly at city or country level can address such issues vital to the electric utilities. In this paper, an artificial neural network based energy consumption forecasting system is proposed and the efficiency of the proposed system is shown with the results of a set of simulation studies. The proposed system can provide valuable inputs to smart grid applications.en_US
dc.description.sponsorshipInst Syst & Technologies Informat Control & Commun,Univ Upper Alsace,IEEE Control Syst Soc,IEEE Robot & Automat Soc,Int Federat Automat Control,EUROMICRO,Assoc Advancement Artificial Intelligence,Associacao Portuguesa Controlo Automatico,Int Neural Network Soc,Asia Pacific Neural Network Assembly,euRobot,ACM Special Interest Grp Artificial Intelligence,AISBLen_US
dc.identifier.endpage116en_US
dc.identifier.isbn978-9-8975-8149-6
dc.identifier.scopus2-s2.0-84943574096en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage110en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14551/23872
dc.identifier.wosWOS:000381618600015en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofIcimco 2015 Proceedings Of The 12th International Conference On Informatics In Control, Automation And Robotics, Vol. 1en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSmart Griden_US
dc.subjectDemand Forecastingen_US
dc.subjectArtificial Neural Networken_US
dc.subjectOptimizationen_US
dc.subjectDemand Responseen_US
dc.titlePI-controlled ANN-based Energy Consumption Forecasting for Smart Gridsen_US
dc.typeConference Objecten_US

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