期刊论文

Xu, PH

英文

Energies

1996-1073

2023

16

21

7250-

10.3390/en16217250

Conceptualization, R.L.; data curation, Y.S. and D.W.; formal analysis, R.L. and P.X.; funding acquisition, Y.L.; investigation, C.Y. and P.X.; methodology, R.L., C.Y. and P.X.; project administration, R.L.; software, R.L. and Y.S.; supervision, C.Y. and Y.L.; writing—original draft, R.L.; writing—review and editing, R.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the Open Project of the Hubei Provincial Key Laboratory of Intelligent Robot: 20220101.

本校为第一机构

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 th...