为提升预测的准确度,提出一种互补集合经验模态分解（CEEMD）、样本熵（SE）、相空间重构（PSR）以及神经网络（BP）的短期风速预测新模型。首先运用CEEMD技术对风速时间序列进行拆解,化繁为简,分离出多个子序列。随后,计算每个子序列的SE,从SE的特征中重组风速序列。继而,将各子序列的预测结果进行相空间重构,获取神经网络预测的输入输出样本。最后运用神经网络预测每个样本,并将所有预测结果累加。此外,还对风电场的实际运行数据进行试验,并将模型的预测结果与其他预测方法进行对比,实验结果显示出此模型在提高风速预测精度方面的显著优势。
Abstract
Accurate and reliable wind speed forecasting is vital for maintaining the stability of power systems. To improve prediction accuracy, this study introduces a novel short-term wind speed prediction model that integrates complementary ensemble empirical mode decomposition （CEEMD）, sample entropy （SE）, phase space reconstruction （PSR）, and a backpropagation neural network （BP）. Initially, CEEMD is employed to decompose the wind speed time series into multiple intrinsic mode functions （IMFs）, thereby simplifying the data structure. Following this, the SE of each IMF is calculated, and the wind speed sequence is reconstructed based on SE characteristics. Subsequently, the prediction results of each IMF undergo phase space reconstruction, yielding input-output samples for neural network prediction. Finally, the BP neural network is utilized to forecast each sample, and the predicted values are aggregated. The proposed model is evaluated using real-world data from a wind farm, and its performance is compared with other prediction methods. Experimental results demonstrate that this model significantly enhances wind speed prediction accuracy.
关键词
风速预测 /
样本熵 /
互补集合经验模态分解 /
相空间重构 /
神经网络 /
时间序列
Key words
wind speed forecasting /
sample entropy /
complementary ensemble empirical mode decomposition /
phase space reconstruction /
neural networks /
time series
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参考文献
[1] YADAV G R, MUNEENDER E, SANTHOSH M.Wind speed prediction using hybrid long short-term memory neural network based approach[C]//2021 International Conference on Sustainable Energy and Future Electric Transportation (SEFET), Hyderabad, India, 2021: 1-6.
[2] RACHMATULLAH M I C, SANTOSO J, SURENDRO K. Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction[J]. PeerJ computer science, 2021, 7: e724.
[3] MADHIARASAN M.Accurate prediction of different forecast horizons wind speed using a recursive radial basis function neural network[J]. Protection and control of modern power systems, 2020, 5: 22.
[4] JIAO X G, YANG Q M, XU B.Hybrid intelligent feedforward-feedback pitch control for VSWT with predicted wind speed[J]. IEEE transactions on energy conversion, 2021, 36(4): 2770-2781.
[5] ZHANG L, HE S, CHENG J, et al.Research on neural network wind speed prediction model based on improved sparrow algorithm optimization[J]. Energy reports, 2022, 8: 739-747.
[6] OFORI-NTOW JNR E, ZIGGAH Y Y, RODRIGUES M J, et al. A hybrid chaotic-based discrete wavelet transform and Aquila optimisation tuned-artificial neural network approach for wind speed prediction[J]. Results in engineering, 2022, 14: 100399.
[7] KINGSY GRACE R, MANIMEGALAI R.Design of neural network based wind speed prediction model using GWO[J]. Computer systems science and engineering, 2022, 40(2): 593-606.
[8] SAREEN K, PANIGRAHI B K, SHIKHOLA T, et al.An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction[J]. Energy, 2023, 278: 127799.
[9] 李秉晨, 于惠钧, 丁华轩, 等. 基于CEEMD和LSTM-ARIMA的短期风速预测[J]. 中国测试, 2022, 48(2): 163-168.
LI B C, YU H J, DING H X, et al.Short-term wind speed prediction based on CEEMD and LSTM-ARIMA[J]. China measurement & test, 2022, 48(2): 163-168.
[10] 高桂革, 原阔, 曾宪文, 等. 基于改进CEEMD-CS-ELM的短期风速预测[J]. 太阳能学报, 2021, 42(7): 284-289.
GAO G G, YUAN K, ZENG X W, et al.Short-term wind speed prediction based on improved CEEMD-CS-ELM[J]. Acta energiae solaris sinica, 2021, 42(7): 284-289.
[11] 赵鑫, 陈臣鹏, 毕贵红, 等. 基于PAM-SSD-LSTM的短期风速预测[J]. 太阳能学报, 2023, 44(1): 281-288.
ZHAO X, CHEN C P, BI G H, et al.Short-term wind speed prediction based on PAM-SSD-LSTM[J]. Acta energiae solaris sinica, 2023, 44(1): 281-288.
[12] 王贺, 陈蕻峰, 熊敏, 等. 融合CEEMDAN和ICS-LSTM的短期风速预测建模[J]. 电子测量与仪器学报, 2022, 36(4): 17-23.
WANG H, CHEN H F, XIONG M, et al.Short-term wind speed forecasting modeling integrating CEEMDAN and ICS-LSTM[J]. Journal of electronic measurement and instrumentation, 2022, 36(4): 17-23.
[13] 付文龙, 章轩瑞, 张海荣, 等. 基于INGO-SWGMN混合模型的超短期风速预测研究[J]. 太阳能学报, 2024, 45(5): 133-143.
FU W L, ZHANG X R, ZHANG H R, et al.Ultra-short-term wind speed prediction based ONINGO-SWGMN hybrid model[J]. Acta energiae solaris sinica, 2024, 45(5): 133-143.
[14] PACKARD N H, CRUTCHFIELD J P, SHAW R S.Geometry from a time series[J]. Physical review letters, 2008, 45: 712.
[15] 何坚, 王晓芳. 基于ARIMA和LS-SVM组合模型的短期风速预测[J]. 机电工程技术, 2023, 52(8): 30-34.
HE J, WANG X F.Short-term wind speed prediction based on ARIMA and LS-SVM composite model[J]. Mechanical & electrical engineering technology, 2023, 52(8): 30-34.
[16] LI J L, SONG Z H, WANG X F, et al.A novel offshore wind farm typhoon wind speed prediction model based on PSO-Bi-LSTM improved by VMD[J]. Energy, 2022, 251: 123848.
[17] 张琰妮, 史加荣, 李津, 等. 融合残差与VMD-ELM-LSTM的短期风速预测[J]. 太阳能学报, 2023, 44(9): 340-347.
ZHANG Y N, SHI J R, LI J, et al.Short-term wind speed prediction based on residualand VMD-ELM-LSTM[J]. Acta energiae solaris sinica, 2023, 44(9): 340-347.
[18] 田崇翼, 王学睿, 王瑞琪. 基于CEEMD-SVM的风速混合预测模型研究[J]. 计算机时代, 2023(7): 24-28.
TIAN C Y, WANG X R, WANG R Q.Research on hybrid wind speed prediction model based on CEEMD-SVM[J]. Computer era, 2023(7): 24-28.
基金
云南省科技厅基础研究项目（202401BD070001-072）; 云南省教育厅科学研究项目（2024J0453）; 云南农业大学大学生创新项目（XJ2023240）
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