Enhanced photovoltaic power forecasting in renewable energy systems using genetic algorithm and SVM approaches

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dc.contributor.authorBüyükbıçakcı, Erdal
dc.contributor.authorAtlı, Cahit
dc.contributor.authorDumanlı, Metin
dc.contributor.authorBulat, Selçuk
dc.date.accessioned2025-04-08T05:14:13Z
dc.date.available2025-04-08T05:14:13Z
dc.date.issued2024
dc.departmentMeslek Yüksekokulu, Gedik Meslek Yüksekokulu, Sivil Havacılık Kabin Hizmetleri Programı
dc.description4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024, Tumkuru -- 4-5 December 2024
dc.description.abstractModern energy management struggles to integrate renewable energy (RE) resources like wind and solar electricity into power networks. Accurate power forecasting models improve grid reliability and stability, helping solve this problem. This research analyses renewable energy power forecasting models' literature, focusing on significant improvements in the last decade.High-quality statistical error metrics-based forecasting model research articles are examined. The proposed system includes data analysis, feature engineering, and model training. Feature engineering includes normalization, PCA, and WD. To increase the feature set, photovoltaic (PV) and wind power generating factors are considered. The GA-SVM-based model forecasted solar power outputs in renewable energy systems with 93.18% training accuracy. Innovative renewable energy forecasting methods, including feature engineering and model optimization, are reviewed in this article. For optimal RE resource integration into power networks, renewable energy forecasting must be more accurate and efficient.
dc.identifier.doi10.1109/ICMNWC63764.2024.10872032
dc.identifier.isbn9798350352931
dc.identifier.scopus2-s2.0-105000142276
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ICMNWC63764.2024.10872032
dc.identifier.urihttps://hdl.handle.net/11501/2083
dc.indekslendigikaynakScopus
dc.institutionauthorAtlı, Cahit
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGenetic Algorithm (GA)
dc.subjectPhotovoltaic Power Forecasting
dc.subjectRenewable Energy
dc.titleEnhanced photovoltaic power forecasting in renewable energy systems using genetic algorithm and SVM approaches
dc.typeConference Object

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