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

Arda Sen; Cenk Andic; Esra Aydin; Mikail Purlu; Belgin Turkay

doi:10.1109/ELECO60389.2023.10416064

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

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

^*Bu çalışma için yazışmadan sorumlu yazar

Elektrik Mühendisliği Bölümü

Istanbul Technical University
Cumhuriyet University

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

2 Atıf (Scopus)

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 dil	İngilizce
Ana bilgisayar yayını başlığı	14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9798350360493
DOI'lar	https://doi.org/10.1109/ELECO60389.2023.10416064
Yayın durumu	Yayınlandı - 2023
Etkinlik	14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Virtual, Bursa, Türkiye Süre: 30 Kas 2023 → 2 Ara 2023

Yayın serisi

Adı	14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	14th International Conference on Electrical and Electronics Engineering, ELECO 2023
Ülke/Bölge	Türkiye
Şehir	Virtual, Bursa
Periyot	30/11/23 → 2/12/23

Bibliyografik not

Publisher Copyright:
© 2023 IEEE.

BM SKH

Bu sonuç, aşağıdaki Sürdürülebilir Kalkınma Hedefine/Hedeflerine katkıda bulunur

SKH 7 Erişilebilir ve Temiz Enerji

Belgeye Erişim

10.1109/ELECO60389.2023.10416064

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Sen, A., Andic, C., Aydin, E., Purlu, M., & Turkay, B. (2023). Forecasting of Wind Turbine Output Power with Artificial Neural Network in Izmir, Türkiye. In 14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings (14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ELECO60389.2023.10416064

Sen, Arda ; Andic, Cenk ; Aydin, Esra et al. / Forecasting of Wind Turbine Output Power with Artificial Neural Network in Izmir, Türkiye. 14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings).

@inproceedings{ff82208830e2440bad065c56f9a5e658,

title = "Forecasting of Wind Turbine Output Power with Artificial Neural Network in Izmir, T{\"u}rkiye",

abstract = "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.",

keywords = "artificial neural networks, forecasting, wind power",

author = "Arda Sen and Cenk Andic and Esra Aydin and Mikail Purlu and Belgin Turkay",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 14th International Conference on Electrical and Electronics Engineering, ELECO 2023 ; Conference date: 30-11-2023 Through 02-12-2023",

year = "2023",

doi = "10.1109/ELECO60389.2023.10416064",

language = "English",

series = "14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings",

address = "United States",

}

Sen, A, Andic, C , Aydin, E, Purlu, M & Turkay, B 2023, Forecasting of Wind Turbine Output Power with Artificial Neural Network in Izmir, Türkiye. in 14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings. 14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 14th International Conference on Electrical and Electronics Engineering, ELECO 2023, Virtual, Bursa, Türkiye, 30/11/23. https://doi.org/10.1109/ELECO60389.2023.10416064

Forecasting of Wind Turbine Output Power with Artificial Neural Network in Izmir, Türkiye. / Sen, Arda; Andic, Cenk ; Aydin, Esra et al.
14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

TY - GEN

T1 - Forecasting of Wind Turbine Output Power with Artificial Neural Network in Izmir, Türkiye

AU - Sen, Arda

AU - Andic, Cenk

AU - Aydin, Esra

AU - Purlu, Mikail

AU - Turkay, Belgin

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - artificial neural networks

KW - forecasting

KW - wind power

UR - https://www.scopus.com/pages/publications/85185833756

U2 - 10.1109/ELECO60389.2023.10416064

DO - 10.1109/ELECO60389.2023.10416064

M3 - Conference contribution

AN - SCOPUS:85185833756

T3 - 14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings

BT - 14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 14th International Conference on Electrical and Electronics Engineering, ELECO 2023

Y2 - 30 November 2023 through 2 December 2023

ER -

Sen A, Andic C , Aydin E, Purlu M, Turkay B. Forecasting of Wind Turbine Output Power with Artificial Neural Network in Izmir, Türkiye. In 14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings). doi: 10.1109/ELECO60389.2023.10416064

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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

BM SKH

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap