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Improvement of wind power prediction from meteorological characterization with machine learning models

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  • Sasser, Christiana
  • Yu, Meilin
  • Delgado, Ruben

Abstract

To mitigate uncertainties in wind resource assessments and to improve the estimation of energy production of a wind project, this work uses a decision tree machine learning model to assess the effectiveness of hub-height wind speed, rotor-equivalent wind speed, and lapse rate as variables in power prediction. Atmospheric data is used to train regression trees and correlate the power outputs to wind profiles and meteorological characteristics to be able to predict power responses according to physical patterns. The decision tree model was trained for four vertical wind profile classifications to showcase the need for multiple calculations of wind speed at various levels of the rotor layer. Results indicate that when compared to traditional power curve methods, the decision tree combining rotor-equivalent wind speed and lapse rate improves prediction accuracy by 22% for the given data-set, while also proving to be the most effective method in power prediction for all classified vertical wind profile types. Models incorporating lapse rate into predictions performed better than those without it, showing the importance of considering atmospheric criteria in wind power prediction analyses.

Suggested Citation

  • Sasser, Christiana & Yu, Meilin & Delgado, Ruben, 2022. "Improvement of wind power prediction from meteorological characterization with machine learning models," Renewable Energy, Elsevier, vol. 183(C), pages 491-501.
    • Handle: RePEc:eee:renene:v:183:y:2022:i:c:p:491-501
    DOI: 10.1016/j.renene.2021.10.034
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    References listed on IDEAS

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    Cited by:

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    2. Song, Dongran & Li, Ziqun & Wang, Lei & Jin, Fangjun & Huang, Chaoneng & Xia, E. & Rizk-Allah, Rizk M. & Yang, Jian & Su, Mei & Joo, Young Hoon, 2022. "Energy capture efficiency enhancement of wind turbines via stochastic model predictive yaw control based on intelligent scenarios generation," Applied Energy, Elsevier, vol. 312(C).
    3. Xing, Zhikai & He, Yigang, 2023. "Multi-modal multi-step wind power forecasting based on stacking deep learning model," Renewable Energy, Elsevier, vol. 215(C).
    4. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    5. Zheng, Xidong & Bai, Feifei & Zhuang, Zhiyuan & Chen, Zixing & Jin, Tao, 2023. "A new demand response management strategy considering renewable energy prediction and filtering technology," Renewable Energy, Elsevier, vol. 211(C), pages 656-668.
    6. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.

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