Long term energy consumption forecasting using genetic programming

dc.contributor.author Korhan Karabulut
dc.contributor.author Ahmet Alkan
dc.contributor.author Ahmet Serdar Yilmaz
dc.date.accessioned 2025-10-06T17:53:20Z
dc.date.issued 2008
dc.description.abstract Managing electrical energy supply is a complex task. The most important part of electric utility resource planning is forecasting of the future load demand in the regional or national service area. This is usually achieved by constructing models on relative information such as climate and previous load demand data. In this paper a genetic programming approach is proposed to forecast long term electrical power consumption in the area covered by a utility situated in the southeast of Turkey. The empirical results demonstrate successful load forecast with a low error rate. © Association for Scientific Research. © 2020 Elsevier B.V. All rights reserved.
dc.identifier.doi 10.3390/mca13020071
dc.identifier.issn 22978747, 1300686X
dc.identifier.issn 2297-8747
dc.identifier.uri https://www.scopus.com/inward/record.uri?eid=2-s2.0-39549086785&doi=10.3390%2Fmca13020071&partnerID=40&md5=4643579f5c271f4024ba40e5cd18d20f
dc.identifier.uri https://gcris.yasar.edu.tr/handle/123456789/10365
dc.language.iso English
dc.publisher Association for Scientific Research
dc.relation.ispartof Mathematical and Computational Applications
dc.source Mathematical and Computational Applications
dc.subject Genetic Programming, Load Forecasting, Symbolic Regression, Electric Loads, Electric Power Utilization, Energy Management, Regression Analysis, Resource Allocation, Electrical Energy Supply, Load Forecasting, Symbolic Regression, Genetic Programming
dc.subject Electric loads, Electric power utilization, Energy management, Regression analysis, Resource allocation, Electrical energy supply, Load forecasting, Symbolic regression, Genetic programming
dc.title Long term energy consumption forecasting using genetic programming
dc.type Article
dspace.entity.type Publication
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gdc.collaboration.industrial false
gdc.description.endpage 80
gdc.description.startpage 71
gdc.description.volume 13
gdc.identifier.openalex W2417189808
gdc.index.type Scopus
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gdc.oaire.influence 4.113423E-9
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gdc.oaire.keywords genetic programming; load forecasting; symbolic regression
gdc.oaire.popularity 4.307001E-9
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
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gdc.opencitations.count 18
gdc.plumx.crossrefcites 16
gdc.plumx.mendeley 41
gdc.plumx.scopuscites 45
oaire.citation.endPage 80
oaire.citation.startPage 71
person.identifier.scopus-author-id Karabulut- Korhan (17346083500), Alkan- Ahmet (56261391700), Yilmaz- Ahmet Serdar (55212131300)
publicationissue.issueNumber 2
publicationvolume.volumeNumber 13
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