Citation: Wang, Q.; Huang, L.;
Huang, J.; Liu, Q.; Chen, L.; Liang, Y.;
Liu, P.X.; Li, C. A Hybrid Generative
Adversarial Network Model for Ultra
Short-Term Wind Speed Prediction.
Sustainability 2022, 14, 9021. https://
doi.org/10.3390/su14159021
Academic Editors:
Luis Hernández-Callejo,
Sergio Nesmachnow and
Sara Gallardo Saavedra
Received: 5 June 2022
Accepted: 20 July 2022
Published: 22 July 2022
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Article
A Hybrid Generative Adversarial Network Model for Ultra
Short-Term Wind Speed Prediction
Qingyuan Wang
1,†
, Longnv Huang
1,†
, Jiehui Huang
1,†
, Qiaoan Liu
2
, Limin Chen
1,
* , Yin Liang
1
,
Peter X. Liu
1,3
and Chunquan Li
1
1
The School of Information Engineering, Nanchang University, Nanchang 330031, China;
7904119023@email.ncu.edu.cn (Q.W.); 6105119006@email.ncu.edu.cn (L.H.);
7803018161@email.ncu.edu.cn (J.H.); liangyin@ncu.edu.cn (Y.L.); xpliu@sce.carleton.ca (P.X.L.);
lichunquan@ncu.edu.cn (C.L.)
2
The School of Future Technology, Nanchang University, Nanchang 330031, China;
5811121050@email.ncu.edu.cn
3
The Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
* Correspondence: chenlimin@ncu.edu.cn
† These authors contributed equally to this work.
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
1. Introduction
Energy demand has always been one of the main problems of human development
since the increasing consumption of energy with the improvement of living standards. In
recent years, renewable energy has gradually become a research hotspot. Wind energy is
valued for its clean, pollution-free, renewable, and abundant availability. However, wind is
highly random and volatile, which may affect the stability of the power system and hinder
the efficient use of wind energy [
1
]. Accurate ultra-short-term wind speed prediction
models are therefore crucial in power dispatch planning and power market operations [
2
].
Thus, reliable wind speed prediction has drawn a lot of interest.
The three common wind speed prediction models are physical models, statistical
models, and hybrid models. Physical models take into account the physical conditions and
locations of wind farms, which require abundant meteorological data. Numerical weather
prediction is a typical physical model, as it takes into account temperature pressure and
obstacles for wind speed prediction, so it has a long calculation period [
3
]. Physical models
Sustainability 2022, 14, 9021. https://doi.org/10.3390/su14159021 https://www.mdpi.com/journal/sustainability
