Neural network wind speed prediction based on multiple prediction model and nonlinear combination

Jiajun WANG; Wei CAO; Guilong ZHANG; Huaizhi ZHANG; Zixing LING; Xiaoqiang ZHAO

doi:10.11959/j.issn.2096-3750.2021.00221

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Neural network wind speed prediction based on multiple prediction model and nonlinear combination

Theory and Technology | 更新时间：2024-06-05

- Neural network wind speed prediction based on multiple prediction model and nonlinear combination
- Chinese Journal on Internet of Things Vol. 5, Issue 4, Pages: 81-89(2021)
- 作者机构：
  
  1. 西安邮电大学，陕西西安 710121
  2. 雅砻江流域水电开发有限公司，四川成都 610051
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2021.00221
  CLC： TP183
- Published：30 December 2021，
  
  Published Online：2021-12，
- 稿件说明：
移动端阅览
JIAJUN WANG, WEI CAO, GUILONG ZHANG, et al. Neural network wind speed prediction based on multiple prediction model and nonlinear combination. [J]. Chinese journal on internet of things, 2021, 5(4): 81-89.
DOI：

JIAJUN WANG, WEI CAO, GUILONG ZHANG, et al. Neural network wind speed prediction based on multiple prediction model and nonlinear combination. [J]. Chinese journal on internet of things, 2021, 5(4): 81-89. DOI： 10.11959/j.issn.2096-3750.2021.00221.

摘要

针对复杂山地风速在时空特性上存在强随机性的问题，为了提高风速数据预测的准确性，提出一种基于多预测模型与非线性组合的神经网络风速预测算法。在算法第一层，利用灰狼优化器（GWO）并引入动态收敛因子改进鲸鱼优化算法（WOA），将改进后的 WOA 应用于反向传播神经网络（BPNN）权值及偏置项的更新过程。同时，基于改进后的鲸鱼优化算法的反向传播神经网络（IWOABP）、极限学习机（ELM）和长短期记忆（LSTM） 3种优势互补的方法构建组合预测方法，并在此基础上利用算法第二层的ELM混合机制，以非线性方式学习第一层与最终结果的关系。仿真结果表明，所提算法相较于BPNN、小波神经网络（WNN）及灰狼优化器导向的BPNN （GWOBP），预测误差均有所降低。

Abstract

For the problem of strong randomness in space-time characteristics of wind speed in complex mountains，in order to improve the accuracy of wind speed data prediction

a neural network wind speed prediction algorithm based on multi prediction model and nonlinear combination was proposed.In the first layer of the algorithm

the grey wolf optimizer (GWO) and the dynamic convergence factor were used to improve the whale optimization algorithm (WOA)

and the improved WOA was applied to the updating process of BPNN weights and bias items.At the same time

the improved whale optimiza-tion algorithm of back propagation neural network (IWOABP)

ELM and LSTM three complementary single methods were constructed to build a combination prediction method

and on this basis

the ELM mixing mechanism of the second layer of the algorithm was utilized to learn the relationship between the first layer and the final result in a non-linear way.Simulation results show that compared with BPNN

WNN and GWOBP

the proposed algorithm has lower prediction errors.

关键词

复杂山地风速预测鲸鱼优化算法神经网络

Keywords

complex mountain areaswind speed predictionwhale optimization algorithmneural network

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