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A novel model based on multiple input factors and variance reciprocal: application on wind speed forecasting

  • Application of soft computing
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Abstract

Wind energy is an important green energy. The use of wind energy can alleviate the pressure caused by the shortage of traditional energy. The wind speed is affected by many factors, which makes it difficult to forecast accurately. Most wind speed forecasting methods only consider the wind speed data. The other influence factors are usually ignored. In this paper, we use traditional wind speed data and weather factors as the input of our proposed model. In the proposed model, wavelet threshold is employed to reduce the noise of raw wind speed data. We apply grey relational analysis to select the weather factors that have great influence on wind speed. The wind speed data are the input vectors of Elman neural network (Elman) and back propagation optimized by cuckoo search. Weather factors are the input vectors of WNN-GRNN which combines the wavelet neural network (WNN) and general regression neural network (GRNN). Finally, the forecasting results of the weather factors and wind speed data are combined by variance reciprocal method. The data sets at Kalaeloa Oahu, Hawaii are chosen to test the validity of the proposed model. The results show that the proposed model has obvious advantages compared to other benchmark forecasting models.

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Abbreviations

GRA:

Grey relational analysis

CS:

Cuckoo search

WNN:

Wavelet neural network

GRNN:

General regression neural network

BPNN:

Back propagation neural network

ANN:

Artificial neural network

MSE:

Mean squared error

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

SNR:

Signal-to-noise ratio

GA:

Genetic algorithm

PSO:

Particle swarm optimization

SVM:

Support vector machine

LSSVM:

Least square support vector machine

RBF:

Radial basis function

ELM:

Extreme learning machine

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Acknowledgements

This research is supported by National Key R&D Program of China (Grant Number 2018YFB1003205, 2017YFE0111900), the China Postdoctoral Science Foundation, 2021M702943, and 2019 Gansu key talent project.

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Authors and Affiliations

  1. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, 450000, China

    Zhihao Shang & Yanhua Chen

  2. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Min Li, Caihong Li, Yi Yang & Lian Li

Authors
  1. Zhihao Shang
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  2. Min Li
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  3. Yanhua Chen
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  4. Caihong Li
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  5. Yi Yang
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  6. Lian Li
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Contributions

Conceptualization, ZHS and ML; Methodology, ML; Software, ZHS; Validation, ML, YY; Formal Analysis, CHL and ML; Investigation, LL; Resources, YY; Data Curation, YY; Writing-Review and Editing, ZHS and YHC; Visualization, ML and ZHS; Supervision, HYC; Funding Acquisition, YY.

Corresponding author

Correspondence to Min Li.

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Shang, Z., Li, M., Chen, Y. et al. A novel model based on multiple input factors and variance reciprocal: application on wind speed forecasting. Soft Comput 26, 8857–8877 (2022). https://doi.org/10.1007/s00500-021-06661-w

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