Short-Term Solar Power Forecasting Based on CEEMDAN and Kernel Extreme Learning Machine

dc.authorid0000-0002-8257-0829
dc.authorid0000-0002-4576-1941
dc.authorid0000-0002-5364-6265
dc.authorid0000-0003-2618-2861
dc.authorscopusid55973148400
dc.authorscopusid6507098373
dc.contributor.authorGün, Ali Riza
dc.contributor.authorDokur, Emrah
dc.contributor.authorYüzgeç, Uğur
dc.contributor.authorKurban, Mehmet
dc.date.accessioned2023-11-07T07:51:48Z
dc.date.available2023-11-07T07:51:48Z
dc.date.issued2023en_US
dc.departmentEnstitüler, Fen Bilimleri Enstitüsü, Elektronik ve Bilgisayar Mühendisliği
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü
dc.description.abstractThe use of renewable energy sources contributes to environmental awareness and sustainable development policy. The inexhaustible and nonpolluting nature of solar energy has attracted worldwide attention. Accurate forecasting of solar power is vital for the reliability and stability of power systems. However, the effect of the intermittency nature of solar radiation makes the development of accurate prediction models challenging. This paper presents a hybrid model based on Kernel Extreme Learning Machine (Kernel-ELM) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for short-term solar power forecasting. The decomposition technique increases the number of stable, stationary, and regular patterns of the original signals. Each decomposed signal is fed into Kernel- ELM. To validate the performance of the hybrid model, solar power data from the BSEU Renewable Energy Laboratory, measured at 5-minute intervals, are used. To validate the proposed model, its performance is compared to some state-of- the-art forecasting models with seasonal data. The results highlight the good performance of the proposed hybrid model compared to other classical algorithms according to the metrics.en_US
dc.identifier.citationGun, A. R., Dokur, E., Yuzgec, U., & Kurban, M. (2023). Short-Term Solar Power Forecasting Based on CEEMDAN and Kernel Extreme Learning Machine. Elektronika Ir Elektrotechnika, 29(2), 28-34. https://doi.org/10.5755/j02.eie.33856en_US
dc.identifier.doi10.5755/j02.eie.33856
dc.identifier.endpage34en_US
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85163944354
dc.identifier.scopusqualityQ3
dc.identifier.startpage28en_US
dc.identifier.urihttps://doi.org/10.5755/j02.eie.33856
dc.identifier.urihttps://hdl.handle.net/11552/3204
dc.identifier.volume29en_US
dc.identifier.wosWOS:000999128100004
dc.identifier.wosqualityQ3
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWoS
dc.indekslendigikaynakWoS - Science Citation Index Expanded
dc.institutionauthorGün, Ali Riza
dc.institutionauthorDokur, Emrah
dc.institutionauthorYüzgeç, Uğur
dc.institutionauthorKurban, Mehmet
dc.language.isoen
dc.publisherKaunas University of Technologyen_US
dc.relation.ispartofElektronika ir Elektrotechnika
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDecompositionen_US
dc.subjectEnergyen_US
dc.subjectForecasten_US
dc.subjectHybrid Methoden_US
dc.subjectSolar Energyen_US
dc.titleShort-Term Solar Power Forecasting Based on CEEMDAN and Kernel Extreme Learning Machine
dc.typeArticle

Dosyalar

Orijinal paket

Listeleniyor 1 - 1 / 1
Yükleniyor...
Küçük Resim
İsim:
doi.org10.5755j02.eie.33856.pdf
Boyut:
1.94 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Yayıncı Kopyası_Makale

Lisans paketi

Listeleniyor 1 - 1 / 1
Yükleniyor...
Küçük Resim
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama: