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A hybrid prediction model for forecasting wind energy resources

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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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Correspondence to Yagang Zhang.

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Responsible Editor: Philippe Garrigues

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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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  • DOI: https://doi.org/10.1007/s11356-020-08452-6

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