A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey

Basit Öğe Kaydı
dc.authoridGuerrero, Josep/0000-0001-5236-4592
dc.authoridUNSAL, DERYA BETUL/0000-0002-7657-7581
dc.authoridoyucu, saadin/0000-0003-3880-3039
dc.authoridGuler, Merve/0000-0003-4812-948X
dc.authoridAKSOZ, Ahmet/0000-0002-2563-1218
dc.contributor.authorUnsal, Derya Betul
dc.contributor.authorAksoz, Ahmet
dc.contributor.authorOyucu, Saadin
dc.contributor.authorGuerrero, Josep M.
dc.contributor.authorGuler, Merve
dc.date.accessioned2024-10-26T18:09:03Z
dc.date.available2024-10-26T18:09:03Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractFossil fuels still have emerged as the predominant energy source for power generation on a global scale. In recent years, Turkey has experienced a notable decrease in the production of coal and natural gas energy, juxtaposed with a significant rise in the production of renewable energy sources. The study employed neural networks, ANNs (artificial neural networks), and LSTM (long short-term memory), as well as CNN (convolutional neural network) and hybrid CNN-LSTM designs, to assess Turkey's energy potential. Real-time outcomes were produced by integrating these models with meteorological data. The objective was to design strategies for enhancing performance by comparing various models of outcomes. The data collected for Turkey as a whole are based on average values. Machine learning approaches were employed to mitigate the error rate seen in the acquired outcomes. Comparisons were conducted across light gradient boosting machine (LightGBM), gradient boosting regressor (GBR), and random forest regressor (RF) techniques, which represent machine learning models, alongside deep learning models. Based on the findings of the comparative analyses, it was determined that the machine learning model, LightGBM, exhibited the most favorable performance in enhancing the accuracy of predictions. Conversely, the hybrid model, CNN-LSTM, had the greatest rate of inaccuracy. This study will serve as a guide for renewable energy researchers, especially in developing countries such as Turkey that have not switched to a smart grid system.
dc.description.sponsorshipEuropean Union
dc.description.sponsorshipNo Statement Available
dc.identifier.doi10.3390/su16072894
dc.identifier.issn2071-1050
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85190293561
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/su16072894
dc.identifier.urihttps://hdl.handle.net/20.500.12418/29930
dc.identifier.volume16
dc.identifier.wosWOS:001200933200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSustainability
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectrenewable energy
dc.subjectsmart grid
dc.subjectdeep learning
dc.subjectANN
dc.subjectLSTM
dc.subjectCNN
dc.subjectLightGBM
dc.subjectGBR
dc.subjectRF
dc.titleA Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu