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Hybrid neural network wind speed prediction based on two-level decomposition and weighted averaging

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Abstract

The randomicity and fluctuation of the wind speed will influence the precision of the forecast. This paper presents a new method of combined wind speed forecast based on the two-level decomposition and weighted average, which can improve the accuracy of wind speed forecasting. First, the improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) decomposition method is used to get different sub-sequences, and then the fuzzy entropy is used to judge the degree of confusion of the sub-sequences. In this paper, the autoregressive integrated moving average (ARIMA) model is used to predict the minimum fuzzy entropy. And the other subsequences are decomposed by backpropagation neural network (BPNN), variational mode decomposition (VMD) and predicted by nonlinear auto-regressive (NAR) and BP neural network with suitable weighting ratio for weighted average and particle swarm optimization- long short-term memory (PSO-LSTM) neural network respectively, and ultimately all the predicted values are superimposed to get the final prediction. Experiments are conducted using three datasets and eight comparison models to verify the validity of this model. The prediction analysis was carried out using the actual measured data of a wind farm in Inner Mongolia, and the results indicated that (1) using fuzzy entropy can effectively improve the prediction precision; (2) the prediction accuracy of the combined prediction method of neural network based on two-level decomposition was greatly improved and the prediction results were more reliable; (3) Decompose one of the subsequences with VMD, predict it with NAR and BP neural network, and choose appropriate weight ratio for weighted average prediction will achieve better prediction results; (4) the root mean square error (RMSE) of the hybrid model on the three wind speed datasets were 0.28777, 0.22786 and 0.17128, which are lower than the comparison values of other models. So, it is workable to use this hybrid model in wind speed prediction.

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Data Availability

Data is provided within the manuscript or supplementary information files.

Abbreviations

AR:

autoregressive model

ARMA:

autoregressive moving average model

ARIMA:

autoregressive integrated moving average

MA:

moving average model

ARIMAX:

auto-regressive integrated moving average with exogenous variables

f-ARIMA:

fractional-ARIMA

NWP:

numerical weather prediction

Anns:

artificial neural networks

FL:

fuzzy logic

SVM:

support vector machine

EMD:

empirical mode decomposition

AOA:

archimedes optimization algorithm

NGO:

northern goshawk optimization

MPA:

marine predators algorithm

HPO:

hunter-prey optimizer

ICEEMDAN:

improved complementary ensemble empirical mode decomposition with adaptive noise

BPNN:

back-propagation neural network

VMD:

variational mode decomposition

NAR:

nonlinear auto-regressive

PSO:

particle swarm optimization

LSTM:

long short-term memory neural network

DM:

Diebold-Mariano test

MCS:

model confidence set

EEMD:

ensemble empirical mode decomposition

CEEMD:

complementary ensemble empirical mode decomposition

CEEMDAN:

complete ensemble empirical mode decomposition with adaptive noise

RNN:

recurrent neural network

MSE:

mean square error

MAE:

mean absolute error

MAPE:

mean absolute percentage error

RMSE:

root mean square error

R\(^2\) :

R-Square

RRSE:

root relative squared error

CORR:

correlation coefficient

DS:

directional symmetry

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Acknowledgements

The authors would like to thank the valuable comments and suggestions of the anonymous reviewers.

Funding

This research was funded by the NSFC (National Natural Science Foundation of China) project (grant number: 42371377).

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Author notes

  1. Yu-long Bai, Zai-hong Hou and Rui Wang These authors contributed equally to this work.

Authors and Affiliations

  1. Northwest Normal University, College of Physics and Electronic Engineering, LanZhou, 730070, GanSu, China

    Qi Bi, Yu-long Bai, Zai-hong Hou & Rui Wang

Authors
  1. Qi Bi
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Contributions

Qi Bi: Conceptualization, Methodology, Writing-original draft preparation, Software,Survey. Yulong Bai: Conceptualization, Supervision, Fund acquisition and editing. Zaihong Hou: Formal analysis, resources. Rui Wang: Verification. All authors read the manuscript and all of them agreed to publish it.

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Correspondence to Yu-long Bai.

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Communicated by: Hassan Babaie.

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Bi, Q., Bai, Yl., Hou, Zh. et al. Hybrid neural network wind speed prediction based on two-level decomposition and weighted averaging. Earth Sci Inform 17, 4213–4232 (2024). https://doi.org/10.1007/s12145-024-01388-2

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