Short term and medium term power distribution load forecasting by neural networks

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dc.authorid0000-0002-8291-1419
dc.contributor.authorYalcinoz, T
dc.contributor.authorEminoglu, U
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2005
dc.departmentNiğde ÖHÜ
dc.description.abstractLoad forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey. (C) 2004 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.enconman.2004.07-005
dc.identifier.endpage1405
dc.identifier.issn0196-8904
dc.identifier.issn1879-2227
dc.identifier.issue45574
dc.identifier.scopus2-s2.0-15744381443
dc.identifier.scopusqualityQ1
dc.identifier.startpage1393
dc.identifier.urihttps://dx.doi.org/10.1016/j.enconman.2004.07-005
dc.identifier.urihttps://hdl.handle.net/11480/5590
dc.identifier.volume46
dc.identifier.wosWOS:000227027800005
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofENERGY CONVERSION AND MANAGEMENT
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectneural networks
dc.subjectshort term
dc.subjectmedium term
dc.subjectpeak
dc.subjectload forecasting
dc.subjectpower distribution systems
dc.titleShort term and medium term power distribution load forecasting by neural networks
dc.typeArticle

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