A hybrid prediction model for forecasting wind energy resources

Zhang, Yagang; Pan, Guifang

doi:10.1007/s11356-020-08452-6

Research Article
Published: 25 March 2020

A hybrid prediction model for forecasting wind energy resources

Yagang Zhang^1,2,3 &
Guifang Pan¹

Environmental Science and Pollution Research volume 27, pages 19428–19446 (2020)Cite this article

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10 Citations
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Abstract

Wind energy is important to the transformation and development of global energy, because it is clean and renewable. However, the productivity of wind power is low due to its volatility, randomness, and uncertainty. Therefore, a new hybrid prediction model based on combined Elman-radial basis function (RBF) and Lorenz disturbance is proposed, which can promote the productivity of wind power by better predicting wind speed, firstly, applying the variational mode decomposition (VMD) algorithm to original nonstationary wind speed data to obtain several relatively stationary intrinsic mode functions (IMF), so as to fully exploit its potential characteristics. Meanwhile, the sample entropy is introduced to determine the decomposition number K. Afterwards, different IMF components with different characteristics are used for training and prediction: Elman neural network with sensitivity to historical state data is used for wind speed trend components; RBF with strong nonlinear mapping capability is adopted for other stochastic modal components. Next, the first-step prediction values can be obtained by reconstructing the predicted results of the respective IMF components. Finally, the Lorenz equation is introduced in view of the effects of atmospheric disturbances on wind fluctuations, which can be used to revise the first-step prediction results to obtain more realistic prediction results. By experimenting with the real data from two different wind farms and comparing with other predictive models, we found that (1) VMD can solve the problem of modal aliasing in empirical mode decomposition, to obtain a better decomposition result; (2) the combined prediction method of Elman and RBF is used for modal components, that is, different algorithms are adopted for different components, which have better prediction effects; (3) in this research, the results of the proposed combination prediction model is more accurate by comparison with the other neural network models. This research work will help the power system to rationally formulate wind farm control strategies, enhance the self-regulation of wind farm, and further promote global energy innovation.

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Abbreviations

ANN:: Artificial neural network
ARMA:: Autoregressive moving average
ARIMA:: Autoregressive integrated moving average
BP:: Back propagation
DBM:: Deep Boltzmann machine
EEMD:: Ensemble empirical mode decomposition
EMD:: Empirical mode decomposition
GA:: Genetic algorithm
GAWNN:: Wavelet neural network optimized by genetic algorithm
GRNN:: General regression neural network
GWEC:: Global Wind Energy Council
IEA:: International Energy Agency
IMF:: Intrinsic mode functions
LCDF:: Lorenz comprehensive disturbance flow
LD:: Lange distance
LS-SVM:: Least squares support vector machine
MAE:: Mean absolute error
MAPE:: Mean absolute percentage error
MKRR:: Multi-kernel robust ridge regression
RBF:: Radial basis function
RMSE:: Root mean square error
SE:: Sample entropy
SVM:: Support vector machine
SVR:: Support vector regression
VMD:: Variational mode decomposition
WT:: Wavelet transform
WD:: Wavelet decomposition

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Acknowledgments

The authors thank distinguished Dr. Philippe Garrigues and the anonymous referees for the thoughtful and constructive suggestions that led to a considerable improvement of the paper.

Funding

This work was supported by the National Natural Science Foundation of China (51637005), the Fundamental Research Funds for the Central Universities (2017MS166), and the Natural Science Foundation of Hebei Province.

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Affiliations

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China
Yagang Zhang & Guifang Pan
Interdisciplinary Mathematics Institute, University of South Carolina, Columbia, SC, 29208, USA
Yagang Zhang
Department of Electrical Engineering, North China Electric Power University, Box 205, Baoding, 071003, Hebei, People’s Republic of China
Yagang Zhang

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Yagang Zhang
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Correspondence to Yagang Zhang.

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Zhang, Y., Pan, G. A hybrid prediction model for forecasting wind energy resources. Environ Sci Pollut Res 27, 19428–19446 (2020). https://doi.org/10.1007/s11356-020-08452-6

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Received: 07 January 2020
Accepted: 16 March 2020
Published: 25 March 2020
Issue Date: June 2020
DOI: https://doi.org/10.1007/s11356-020-08452-6

Keywords

Short-term wind speed prediction
VMD
Sample entropy
Combined Elman-RBF model
Lorenz system