A Hybrid Generative Adversarial Network Model for Ultra Short-Term Wind Speed Prediction

Qingyuan Wang, Longnv Huang, Jiehui Huang, Qiaoan Liu, Limin Chen, Yin Liang, Peter X. Liu and Chunquan Li
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Qingyuan Wang: The School of Information Engineering, Nanchang University, Nanchang 330031, China
Longnv Huang: The School of Information Engineering, Nanchang University, Nanchang 330031, China
Jiehui Huang: The School of Information Engineering, Nanchang University, Nanchang 330031, China
Qiaoan Liu: The School of Future Technology, Nanchang University, Nanchang 330031, China
Limin Chen: The School of Information Engineering, Nanchang University, Nanchang 330031, China
Yin Liang: The School of Information Engineering, Nanchang University, Nanchang 330031, China
Peter X. Liu: The School of Information Engineering, Nanchang University, Nanchang 330031, China
Chunquan Li: The School of Information Engineering, Nanchang University, Nanchang 330031, China

Sustainability, 2022, vol. 14, issue 15, 1-16

Abstract: To improve the accuracy of ultra-short-term wind speed prediction, a hybrid generative adversarial network model (HGANN) is proposed in this paper. Firstly, to reduce the noise of the wind sequence, the raw wind data are decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then the decomposed modalities are entered into the HGANN network for prediction. HGANN is a continuous game between the generator and the discriminator, which in turn allows the generator to learn the distribution of the wind data and make predictions about it. Notably, we developed the optimized broad learning system (OBLS) as a generator for the HGANN network, which can improve the generalization ability and error convergence of HGANN. In addition, improved particle swarm optimization (IPSO) was used to optimize the hyperparameters of OBLS. To validate the performance of the HGANN model, experiments were conducted using wind sequences from different regions and at different times. The experimental results show that our model outperforms other cutting-edge benchmark models in single-step and multi-step forecasts. This demonstrates not only the accuracy and robustness of the proposed model but also the applicability of our model to more general environments for wind speed prediction.

Keywords: wind speed forecast; OBLS; data preprocessing; optimized hyper-parameters (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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