Forecasting of Wind Turbine Output Power with Artificial Neural Network in Izmir, Türkiye

Arda Sen*, Cenk Andic, Esra Aydin, Mikail Purlu, Belgin Turkay

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Özet

This study focuses on one of the most common issues in wind energy, which is the forecasting of wind turbine output power. Using wind speed and turbine output power data collected from a wind turbine in Seferihisar, Izmir at 10-minute intervals over a year, an artificial neural network (ANN) model was trained in MATLAB. In the model, while the wind speed and turbine output power are taken as inputs, the output layer forecasts the wind turbine's output power. With the ANN model, short, medium, and long-term forecasts of the wind turbine's output power were forecasted. The forecasting results of the ANN model exhibit high correlation with the measured values, with regression values for long-term predictions on a monthly basis and medium-term predictions on a daily basis being, respectively, around R=0.97 and R=0.98. These results emphasize that artificial intelligence-based methods can be used as a reliable tool in wind energy forecasts.

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

Bibliyografik not

Publisher Copyright:
© 2023 IEEE.

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