Forecasting Electricity Demand for Turkey: Modeling Periodic Variations and Demand Segregation

dc.contributor.author Yükseltan, Ergün
dc.contributor.author Yücekaya, Ahmet
dc.contributor.author Bilge, Ayşe Hümeyra
dc.date.accessioned 2019-06-27T08:01:20Z
dc.date.available 2019-06-27T08:01:20Z
dc.date.issued 2017
dc.description.abstract In deregulated electricity markets the independent system operator (ISO) oversees the power system and manages the supply and demand balancing process. In a typical day the ISO announces the electricity demand forecast for the next day and gives participants an option to prepare offers to meet the demand. In order to have a reliable power system and successful market operation it is crucial to estimate the electricity demand accurately. In this paper we develop an hourly demand forecasting method on annual weekly and daily horizons using a linear model that takes into account the harmonics of these variations and the modulation of diurnal periodic variations by seasonal variations. The electricity demand exhibits cyclic behavior with different seasonal characteristics. Our model is based solely on sinusoidal variations and predicts hourly variations without using any climatic or econometric information. The method is applied to the Turkish power market on data for the period 2012-2014 and predicts the demand over daily and weekly horizons within a 3% error margin in the Mean Absolute Percentage Error (MAPE) norm. We also discuss the week day/weekend/holiday consumption profiles to infer the proportion of industrial and domestic electricity consumption. (C) 2017 Elsevier Ltd. All rights reserved. en_US]
dc.identifier.doi 10.1016/j.apenergy.2017.02.054 en_US
dc.identifier.issn 0306-2619 en_US
dc.identifier.issn 1872-9118 en_US
dc.identifier.issn 0306-2619
dc.identifier.issn 1872-9118
dc.identifier.scopus 2-s2.0-85013814517 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/346
dc.identifier.uri https://doi.org/10.1016/j.apenergy.2017.02.054
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.relation.ispartof Applied Energy
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Time series analysis en_US
dc.subject Fourier series en_US
dc.subject Electricity demand for Turkey en_US
dc.subject Demand segregation en_US
dc.subject Load forecast en_US
dc.title Forecasting Electricity Demand for Turkey: Modeling Periodic Variations and Demand Segregation en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.institutional Yükseltan, Ergün en_US
gdc.author.institutional Yücekaya, Ahmet en_US
gdc.author.institutional Bilge, Ayşe Hümeyra en_US
gdc.bip.impulseclass C3
gdc.bip.influenceclass C4
gdc.bip.popularityclass C3
gdc.coar.access open access
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.department Fakülteler, İşletme Fakültesi, Yönetim Bilişim Sistemleri Bölümü en_US
gdc.description.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü en_US
gdc.description.endpage 296
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q1
gdc.description.startpage 287 en_US
gdc.description.volume 193 en_US
gdc.description.wosquality Q1
gdc.identifier.openalex W2591180046
gdc.identifier.wos WOS:000398871400023 en_US
gdc.index.type WoS
gdc.index.type Scopus
gdc.oaire.diamondjournal false
gdc.oaire.impulse 39.0
gdc.oaire.influence 9.487828E-9
gdc.oaire.isgreen true
gdc.oaire.keywords Demand segregation
gdc.oaire.keywords Time series analysis
gdc.oaire.keywords Electricity demand for Turkey
gdc.oaire.keywords Fourier series
gdc.oaire.keywords Load forecast
gdc.oaire.popularity 4.971988E-8
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 0211 other engineering and technologies
gdc.oaire.sciencefields 0202 electrical engineering, electronic engineering, information engineering
gdc.oaire.sciencefields 02 engineering and technology
gdc.openalex.collaboration National
gdc.openalex.fwci 5.94625064
gdc.openalex.normalizedpercentile 0.97
gdc.openalex.toppercent TOP 10%
gdc.opencitations.count 85
gdc.plumx.crossrefcites 88
gdc.plumx.mendeley 146
gdc.plumx.scopuscites 96
gdc.relation.journal Applied Energy
gdc.scopus.citedcount 96
gdc.virtual.author Bilge, Ayşe Hümeyra
gdc.virtual.author Yücekaya, Ahmet Deniz
gdc.wos.citedcount 79
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