GAN-Based Abrupt Weather Data Augmentation for Wind Turbine Power Day-Ahead Predictions

Renfeng Liu, Yinbo Song, Chen Yuan, Desheng Wang, Peihua Xu () and Yaqin Li
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Renfeng Liu: School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
Yinbo Song: School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
Chen Yuan: Meteorological Observator of Guizhou Province, Guiyang 550081, China
Desheng Wang: School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
Peihua Xu: Hubei Meteorological Service Center, Wuhan 430205, China
Yaqin Li: School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China

Energies, 2023, vol. 16, issue 21, 1-26

Abstract: This study introduces a data augmentation technique based on generative adversarial networks (GANs) to improve the accuracy of day-ahead wind power predictions. To address the peculiarities of abrupt weather data, we propose a novel method for detecting mutation rates (MR) and local mutation rates (LMR). By analyzing historical data, we curated datasets that met specific mutation rate criteria. These transformed wind speed datasets were used as training instances, and using GAN-based methodologies, we generated a series of augmented training sets. The enriched dataset was then used to train the wind power prediction model, and the resulting prediction results were meticulously evaluated. Our empirical findings clearly demonstrate a significant improvement in the accuracy of day-ahead wind power prediction due to the proposed data augmentation approach. A comparative analysis with traditional methods showed an approximate 5% increase in monthly average prediction accuracy. This highlights the potential of leveraging mutated wind speed data and GAN-based techniques for data augmentation, leading to improved accuracy and reliability in wind power predictions. In conclusion, this paper presents a robust data augmentation method for wind power prediction, contributing to the potential enhancement of day-ahead prediction accuracy. Future research could explore additional mutation rate detection methods and strategies to further enhance GAN models, thereby amplifying the effectiveness of wind power prediction.

Keywords: day-ahead prediction; mutation rate; data augmentation; GAN model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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