A Novel Hybrid Model Proposal derived from Prevalent Methods for Power Generation Prediction of Solar Power Plants

Necati Aksoy*, Istemihan Genç

*Bu çalışma için yazışmadan sorumlu yazar

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Özet

Renewable energy sources play a pivotal role in contemporary distributed energy generation, owing to their significance in reducing energy costs and mitigating carbon emissions. Ensuring predictability in energy demand and production is crucial for effective future planning, wherein intuitive predictions for renewable energy sources are indispensable. In this study, we propose a novel method for predicting power generation in solar power plants. We develop a hybrid prediction model by combining prevalent machine learning models trained with meteorological data, yielding superior results compared to individual model outcomes. Through analysis, we evaluate the performance of the models trained with real meteorological and production data, while emphasizing the advantages of the proposed hybrid approach. The proposed method offers valuable insights into enhancing the predictability of solar power plant generation, thereby contributing to the advancement of renewable energy utilization.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9798350360493
DOI'lar
Yayın durumuYayınlandı - 2023
Etkinlik14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Virtual, Bursa, Turkey
Süre: 30 Kas 20232 Ara 2023

Yayın serisi

Adı14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings

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???event.eventtypes.event.conference???14th International Conference on Electrical and Electronics Engineering, ELECO 2023
Ülke/BölgeTurkey
ŞehirVirtual, Bursa
Periyot30/11/232/12/23

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Publisher Copyright:
© 2023 IEEE.

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