[1]胡亚兰,陈亮,余相,等.基于总体经验模态分解和CoDE－BP短期风速预测[J].计算机技术与发展,2019,29(02):195-201.[doi:10．3969/j．issn．1673－629X．2019．02．041]
　HU Yalan,CHEN Liang,YU Xiang,et al.Short－term Wind Speed Forecasting Based on Ensemble Empirical Mode Decomposition and CoDE－BP Method[J].,2019,29(02):195-201.[doi:10．3969/j．issn．1673－629X．2019．02．041]
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基于总体经验模态分解和CoDE－BP短期风速预测()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年02期
页码:
195-201
栏目:
应用开发研究
出版日期:
2019-02-10

文章信息/Info

Title:
Short－term Wind Speed Forecasting Based on Ensemble Empirical Mode Decomposition and CoDE－BP Method
文章编号:
1673－629X(2019)02－0195－07
作者:
胡亚兰; 陈亮; 余相; 王丹
东华大学 信息科学与技术学院，上海 201620
Author(s):
HU Ya－lan; CHEN Liang; YU Xiang; WANG Dan
School of Information Science and Technology，Donghua University，Shanghai 201620，China
关键词:
短期风速预测; 总体经验模态分解; 组合差分进化算法; 前馈神经网络
Keywords:
short－term wind speed forecasting; ensemble empirical mode decomposition; composit differential evolution; back propaga-tion neural network
分类号:
TP391
DOI:
10．3969/j．issn．1673－629X．2019．02．041
摘要:
预测问题是应用机器学习的研究热点之一，是计算机技术领域在实际工程的重要应用，然而由于风速具有随机性、波动性等特性，导致风速预测存在准确率低的问题。为了提高风速预测的准确性，将总体经验模态分解(EEMD)方法引入到组合差分进化算法(CoDE)和前馈(BP)神经网络中，提出了一种新颖的混合风速预测模型(EEMD－CoDE－BP)。利用 EEMD 将原始风速信号分解成一系列不同频率的子序列 IMFs 和残差序列 r，通过每个子序列训练 CoDE－BP 模型，最终的风速预测结果由每个子序列预测结果等权求和得到。以国内某风电场每 10 min、1 h 采样间隔的风速数据进行 MATLAB仿真，对比包括传统的 Elman 神经网络(ENN)、小波神经网络(WNN)、BP、CoDE－BP 和 EMD－CoDE－BP 等算法，仿真结果表明所提方法能对风速进行准确有效的预测，极大地提高了预测精度，减小了预测误差
Abstract:
Prediction is one of the research hotspots of applied machine learning and an important application of computer technology in practical engineering． However，due to the randomness and volatility of wind speed，the accuracy of wind speed prediction is low． In order to improve the accuracy of wind speed forecasting，we propose a new hybrid wind speed forecasting method by introducing the ensemble empirical mode decomposition (EEMD) into the composit differential evolution (CoDE) and the back propagation (BP) neural network． The original wind signal is decomposed by EEMD into several intrinsic mode functions (IMFs) with different frequencies and one residue，then a CoDE－BP neural network is used to model each of the extracted IMFs． The prediction results of all IMFs can be combined by weighted summation to obtain an aggregated output for wind speed． MATLAB simulation is carried out with wind speed datasets from a certain wind farm in Inner Mongolia at 10 min and 1 h sampling interval． Compared with the traditional Elman neural network (ENN)，wavelet neural network (WNN)，BP，CoDE－BP and EMD－CoDE－BP，it demonstrates that the proposed method can greatly improve the prediction accuracy and reduce prediction error．

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[1]张学军[][],朱丽敏[],黄丽亚[][],等. 基于EEMD和GA-SVM的精神分裂症MEG识别[J].计算机技术与发展,2016,26(08):166.
　ZHANG Xue-jun[][],ZHU Li-min[],HUANG Li-ya[][] CHENG Xie-feng[][]. Recognition of Schizophrenic MEG Based on EEMD and GA-SVM[J].,2016,26(02):166.
[2]勾志竟,徐摇 梅*,年飞翔,等.基于长短期记忆网络的风速预测研究[J].计算机技术与发展,2022,32(S1):103.[doi:10. 3969 / j. issn. 1673-629X. 2022. S1. 022]
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更新日期/Last Update: 2019-02-10