Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (1): 166-174.doi: 10.16088/j.issn.1001-6600.2021060912

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Research on Wind Power Short-term Prediction Based on EEMD-GA-BP Model

ZHU Enwen*, ZHU Anqi, WANG Jiedan, LIU Yujiao   

  1. School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha Hunan 410114, China
  • Received:2021-06-09 Revised:2021-08-05 Online:2022-01-25 Published:2022-01-24

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Abstract: With the rapid development of China’s wind power industry, the scale of wind power grid integration is constantly expanding. Accurate prediction of wind farm output power is an effective way to reduce the impact of wind power fluctuations on the power grid, which can improve power quality, and ensure the stable operation of the power grid. In this paper, the method of box analysis and hot card filling is used to clean and reconstruct the abnormal data in the data set. The BP algorithm is improved by combining genetic algorithm and EEMD decomposition algorithm. According to the comparison of prediction results with different time scales, the EEMD-GA-BP model has higher prediction accuracy and more stable prediction effect compared with the traditional prediction model.

Key words: wind power short-term prediction, back propagation neural network, integrated empirical mode decomposition, genetic algorithm

CLC Number: 

  • O212.1
[1] GWEC. Global wind report 2019[R/OL]. (2020-03-25)[2021-06-09]. https://gwec.net/global-wind-report-2019/.
[2]AMBACHD, SCHMID W. A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting[J]. Energy, 2017, 135: 833-850. DOI: 10.1016/j.energy.2017.06.137.
[3]VELÁZQUEZ S, CARTA J A, MATÍAS J M. Influence of the input layer signals of ANNs on wind power estimation for a target site: a case study [J]. Renewable and Sustainable Energy Reviews, 2011, 15(3): 1556-1566. DOI: 10.1016/j.rser.2010.11.036.
[4]徐遵义, 王俊雪. 基于风速融合和NARX神经网络的短期风电功率预测[J]. 现代电子技术, 2020, 43(9): 166-174. DOI: 10.16652/j.issn.1004-373x.2020.09.039.
[5]WANG J Z, WANG Y, JIANG P. The study and application of a novel hybrid forecasting model: a case study of wind speed forecasting in China[J]. Applied Energy, 2015, 143: 472-488. DOI: 10.1016/j.apenergy.2015.01.038.
[6]程启明, 陈路, 程尹曼, 等. 基于EEMD和LS-SVM模型的风电功率短期预测方法[J]. 电力自动化设备, 2018, 38(5): 27-35. DOI: 10.16081/j.issn.1006-6047.2018.05.004.
[7]BILGILI M, SAHIN B. Wind speed prediction of target station from reference stations data[J]. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2013, 35(5):455-466. DOI: 10.1080/15567036.2010.512906.
[8]季梦凡. 风速和风电场功率组合预测及其不确定性分析[D]. 北京: 华北电力大学, 2018.
[9]CAI H H, WU Z H, HUANG C, et al. Wind power forecasting based on ensemble empirical mode decomposition with generalized regression neural network based on cross-validated method[J]. Journal of Electrical Engineering and Technology, 2019, 14(5): 1823-1829. DOI: 10.1007/s42835-019-00186-x.
[10]KORPRASERTSAK N, LEEPHAKPREEDA T. Robust short-term prediction of wind power generation under uncertainty via statistical interpretation of multiple forecasting models[J]. Energy, 2019, 180: 387-397. DOI: 10.1016/j.energy.2019.05.101.
[11]贾俊平. 统计学[M]. 2版. 北京: 清华大学出版社, 2006.
[12]陶澍. 应用数理统计方法[M]. 北京: 中国环境科学出版社, 1994.
[13]MANDIC D P, REHMAN N, WU Z H, et al. Empirical mode decomposition-based time-frequency analysis of multivariate signals[J]. IEEE Signal Processing Magazine, 2013, 30(6): 74-86. DOI: 10.1109/MSP.2013.2267931.
[14]WU Z H, HUANG N E. Ensemble empirical mode decomposition: a moise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1):1-41. DOI: 10.1142/S1793536909000047.
[15]赵岩. 风电场短期风速及功率预测技术研究[D]. 天津: 天津理工大学, 2018. DOI: 10.7666/d.D01415328.
[16]岳有军, 赵岩, 赵辉, 等. 风电场短期功率预测[J]. 中国科技论文, 2018, 13(11): 1232-1237. DOI: 10.3969/j.issn. 2095-2783.2018.11.006.
[17]邹同华, 高云鹏, 伊慧娟, 等. 基于Thompson tau-四分位和多点插值的风电功率异常数据处理[J]. 电力系统自动化, 2020, 44(15): 156-162. DOI: 10.7500/AEPS20191231003.
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