Machine learning approaches in predicting the wind power output and turbine rotational speed in a wind farm-Reference-Cited by-同舟云学术

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Machine learning approaches in predicting the wind power output and turbine rotational speed in a wind farm

Published:2024-08-25 Issue:1 Volume:46 Page:12084-12110
ISSN:1556-7036
Container-title:Energy Sources, Part A: Recovery, Utilization, and Environmental Effects
language:en
Short-container-title:Energy Sources, Part A: Recovery, Utilization, and Environmental Effects

Author:

Ilhan Akin¹,Tumse Sergen²,Bilgili Mehmet³^ORCID,Sahin Besir²⁴^ORCID

Affiliation:

1. Department of Energy Systems Engineering, Faculty of Engineering and Natural Sciences, Ankara Yildirim Beyazit University, Ankara, Turkey

2. Department of Mechanical Engineering, Engineering Faculty, Cukurova University, Adana, Turkey

3. Department of Mechanical Engineering, Ceyhan Engineering Faculty, Cukurova University, Adana, Turkey

4. Department of Aerospace Engineering, Engineering Faculty, İstanbul Aydin University, İstanbul, Turkey

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15567036.2024.2392890

Reference65 articles.

1. Performance Evaluation of ANN and ANFIS Models for Estimating Garlic Crop Evapotranspiration

2. Wind energy characteristics and wind park installation in Shark El-Ouinat, Egypt

3. Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study

4. Balluff, S., J. Bendfeld, and S. Krauter. 2015. Short term wind and energy prediction for offshore wind farms using neural networks. 4th International Conference on Renewable Energy Research and Applications, Palermo, Italy.

5. Long-Term Wind Speed and Power Forecasting Using Local Recurrent Neural Network Models

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