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

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350360493
DOIs	https://doi.org/10.1109/ELECO60389.2023.10416064
Publication status	Published - 2023
Event	14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Virtual, Bursa, Turkey Duration: 30 Nov 2023 → 2 Dec 2023

Publication series

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

Conference

Conference	14th International Conference on Electrical and Electronics Engineering, ELECO 2023
Country/Territory	Turkey
City	Virtual, Bursa
Period	30/11/23 → 2/12/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

artificial neural networks
forecasting
wind power

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ELECO60389.2023.10416064

Cite this

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).

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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.",

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

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language = "English",

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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, Turkey, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

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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

Abstract

Publication series

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Bibliographical note

Keywords

UN SDGs

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