基于多预测模型与非线性组合的神经网络风速预测

doi:10.11959/j.issn.2096-3750.2021.00221

Abstract

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.

Key words: complex mountain areas, wind speed prediction, whale optimization algorithm, neural network

CLC Number:

TP183

Jiajun WANG, Wei CAO, Guilong ZHANG, Huaizhi ZHANG, Zixing LING, Xiaoqiang ZHAO. 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.

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References 9

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名称	参数	参数值
通用参数	噪声强度	0.01 dB
	最大迭代次数	1 000
	误差精度	0.65×10^-4
MPM-NC-NN	鲸鱼种群数量	20只
	学习率	0.05
	输入层	6层
	输出层	1层
	隐含层	3层
LSTM	学习率	0.04
	输入层	6层
	输出层	1层
	隐含层	5层
ELM	灰狼种群数量	20只
	学习率	0.03
	输入层	6层
	输出层	1层
	隐含层	7层
IWOABP	学习率	0.03
	输入层	6层
	输出层	1层
	隐含层	3层

名称	参数	参数值
通用参数	噪声强度	0.01 dB
	最大迭代次数	1 000
	误差精度	0.64×10^-4
MPM-NC-NN	鲸鱼种群数量	20只
	学习率	0.05
	输入层	6层
	输出层	1层
	隐含层	3层
BPNN	学习率	0.03
	输入层	6层
	输出层	1层
	隐含层	3层
灰狼优化器导向的BPNN	灰狼种群数量	20只
（GWOBP, grey wolf optimizer	学习率	0.07
backpropagation neural）	输入层	6层
	输出层	1层
	隐含层	3层
小波神经网络	学习率	0.04
（WNN, wavelet neural network）	输入层	6层
	输出层	1层
	隐含层	2层

算法	BPNN	GWOBP	WNN	MPM-NC-NN
绝对误差	1.635	0.817	0.316	0.322

算法	MAE	RMSE	MSE
BPNN	1.447	1.492	0.149
GWOBP	1.155	1.341	0.134
WNN	1.122	1.426	0.142
MPM-NC-NN	0.118	0.154	0.015

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

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