Science Journal of Energy Engineering

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Wind Speed Forecasting in China: A Review

Received: 25 January 2015    Accepted: 25 January 2015    Published: 10 February 2015
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Abstract

China’s wind power has developed rapidly in the past few years, the large-scale penetration of which will bring big influence on power systems. The wind speed forecasting research is quite important because it can alleviate the negative impacts. This paper reviews the current wind speed forecasting techniques in China. The literature (written in Chinese) sources and classification were firstly analyzed, and then the wind speed forecasting techniques in China were detailed reviewed from four aspects, which are statistical method, soft computing method, hybrid forecasting method and other forecasting methods. This paper can rich the current research in the field of wind speed forecasting.

DOI 10.11648/j.sjee.s.2015030401.13
Published in Science Journal of Energy Engineering (Volume 3, Issue 4-1, July 2015)

This article belongs to the Special Issue Soft Computing Techniques for Energy Engineering

Page(s) 14-21
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Wind Speed Forecasting, Forecasting Techniques, China

References
[1] 2014 Wind power development report published by GWEC. http://www.gwec.net/wp-content/uploads/2014/04/%E5%85%A8%E7%90%83%E9%A3%8E%E7%94%B5%E7%BB%9F%E8%AE%A1%E6%95%B0%E6%8D%AE2013.pdf
[2] Zhao H, Guo S, Fu L. Review on the costs and benefits of renewable energy power subsidy in China [J]. Renewable and Sustainable Energy Reviews, 2014, 37: 538-549.
[3] http://www.cwpc.cn/cwpp/cn/services/cwpc-news-service/1-2020/
[4] Tascikaraoglu A, Uzunoglu M. A review of combined approaches for prediction of short-term wind speed and power [J]. Renewable and Sustainable Energy Reviews, 2014, 34: 243-254.
[5] Soman, Saurabh S., Hamidreza Zareipour, Om Malik. A review of wind power and wind speed forecasting methods with different time horizons. North American Power Symposium (NAPS), 2010. IEEE, 2010.
[6] Foley A M, Leahy P G, Marvuglia A, et al. Current methods and advances in forecasting of wind power generation [J]. Renewable Energy, 2012, 37(1): 1-8.
[7] DING Ming, ZHANG Lijun, WU Yichun. Wind speed forecast model for wind farms based on time series analysis [J]. Electric Power Automation Equipment, 2005, 25(8): 32-34.
[8] PAN Di-fu, LIU Hui, LI Yan-fei. A wind speed forecasting optimization model for wind farms based on time series analysis and kalman filter algorithm [J]. Power System Technology, 2008, 32(7): 82-86.
[9] Zhang Yanning, Kang Longyun, Zhou Shiqiong, et al. Wavelet analysis applied to wind speed prediction in predicate control system of wind turbine [J]. Acta Energiae Solaris Sinica, 2008, 29(5): 520-524.
[10] WANG Yao-nan, SUN Chun-shun, LI Xin-ran. Short-term wind speed simulation corrected with field measured wind speed [J]. Proceedings of the CSEE, 2008, 28(11): 94-100
[11] WANG Xiao-lan, LI Hui. Effective wind speed forecasting in annual prediction of output power for wind farm [J]. Proceedings of the CSEE, 2010, 30(8): 117-122.
[12] SUN Chun-shun, WANG Yao-nan, LI Xin-ran. A vector autoregression model of hourly wind speed and its applications in hourly wind speed forecasting [J]. Proceedings of the CSEE, 2008, 28(14): 112-117
[13] JIANG Jin-liang, LIN Guang-ming. Automatic station wind speed forecasting based on ARIMA model [J]. Control Theory & Applications, 2008, 25(2): 374-376.
[14] FANG Jiang-xiao, ZHOU Hui, HUANG Mei, T.S. Sidhu. Short-term wind power prediction based on statistical clustering analysis [J]. Power System Protection and Control, 2011, 39(11): 67-73.
[15] YANG Xi-yun, SUN Han-mo. Wind speed prediction in wind farms based on time series model [J]. Journal of Chinese Society of Power Engineering, 2011, 31(3): 203-208.
[16] Liu Xingjie, Mi Zengqiang, Yang Qixun, et al. A novel multi-step prediction for wind speed based on EMD [J]. Acta Energiae Solaris Sinica, 2010, 25(4): 165-170.
[17] DU Ying, LU Ji-ping, LI Qing, et al. Short-Term wind speed forecasting of wind farm based on least square-support vector machine [J]. Power System Technology, 2008, 32(15): 62-66.
[18] ZENG Jie, ZHANG Hua. A wind speed forecasting model based on least squares support vector machine [J]. Power System Technology, 2009, 33(18): 144-147.
[19] Zhang Hua, Zeng Jie. Wind speed forecasting model study based on support vector machine [J]. Acta Energiae Solaris Sinica, 2010, 31(7): 928-932.
[20] WU Dong-liang, WANG Yang, GUO Chuang-xin, et al. Short-term wind speed forecasting in wind farm based on improved GMDH network [J]. Power System Protection and Control, 2011, 39(2): 88-93.
[21] YANG Xiyun, SUN Baojun, ZHANG Xinfang, et al. Short-term wind speed forecasting based on support vector machine with similar data [J]. Proceedings of the CSEE, 2012, 32(4): 35-41.
[22] WU Xing-hua, ZHOU Hui, HUANG Mei. Wind speed and generated power forecasting based on pattern recognition in wind farm [J]. Power System Protection and Control, 2008, 36(1): 27-32.
[23] Huang Xiaohua, Li Deyuan, Lv Wenge, et al. Wind speed forecasting with artificial neural networks model [J]. Acta Energiae Solaris Sinica, 2011, 32(2): 193-197.
[24] FAN Gao-feng, WANG Wei-sheng, LIU Chun. Artificial neural network based wind power short term prediction system [J]. Power System Technology, 2008, 32(22): 72-76.
[25] DU Ying, LU Ji-ping, LI Qing, et al. Short-Term wind speed forecasting of wind farm based on least square-support vector machine [J]. Power System Technology, 2008, 32(15): 61-66.
[26] GUO Chuangxin, WANG Yang, SHEN Yong, et al. Multivariate local prediction method for short-term wind speed of wind farm [J]. Proceedings of the CSEE, 2012, 32(1): 24-31.
[27] CAI Kai, TAN Lun-nong, LI Chun-lin, et al. Short-Term wind speed forecasting combing time series and neural network method [J]. Power System Technology, 2008, 32(8): 82-85.
[28] WANG Xiao-lan, WANG Ming-wei. Short-Term wind speed forecasting based on wavelet decomposition and least square support vector machine [J]. Power System Technology, 2010, 34(1): 179-184.
[29] ZHANG Guoqiang, ZHANG Boming. Wind speed and wind turbine output forecast based on combination method [J]. Automation of Electric Power Systems, 2009 (18): 92-95.
[30] WANG De-ming, WANG Li, ZHANG Guang-ming. Short-term wind speed forecast model for wind farms based on genetic BP neural network [J]. Journal of Zhejiang University (Engineering Science), 2012, 46(5): 837-841.
[31] GAO Shuang, DONG Lei, GAO Yang, et al. Mid-long term wind speed prediction based on rough set theory [J]. Proceedings of the CSEE, 2012, 32(1): 32-37.
[32] PENG Chunhua, LIU Gang, SUN Huijuan. Wind speed forecasting based on wavelet decomposition and differential evolution-support vector machine for wind farms [J]. Electric Power Automation Equipment, 2012, 32(1): 9-13.
[33] YANG Xiu-yuan, XIAO Yang, CHEN Shu-yong. Wind speed and generated power forecasting in wind farm [J]. Proceedings of the CSEE, 2005, 25(11): 1-5.
[34] Zeng Jie, Zhang Hua. Wind speed forecasting model study based on least squares support vector machine and ant colony optimization [J]. Acta Energiae Solaris Sinica, 2011, 32(3): 296-300.
[35] Luo Wen, Wang Lina. Short-Term wind speed forecasting for wind farm [J]. Transactions of China Electrotechnical Society, 2011, 26(7): 68-74.
[36] LI Wenliang, WEI Zhinong, SUN Guoqiang, et al. Multi-interval wind speed forecast model based on improved spatial correlation and RBF neural network [J]. Electric Power Automation Equipment, 2009 (6): 89-92.
[37] LI Ran, CHEN Qian, XU Hong-rui. Wind speed forecasting method based on LS-SVM considering the related factors [J]. Power System Protection and Control, 2010 (21): 146-151.
[38] JIANG Xiaoliang, JIANG Chuanwen, PENG Minghong, et al. A short-term combination wind speed forecasting method considering seasonal periodicity and time-continuity [J]. Automation of Electric Power Systems, 2010 (15): 75-79.
[39] SUN Bin, YAO Hai-tao. The short-term wind speed forecast analysis based on the PSO-LSSVM predict model [J]. Power System Protection and Control, 2012, 40(5): 85-89.
[40] YANG Qi, ZHANG Jianha, Wang Xiangfeng, et al. Wind speed and wind power generation forecast based on Wavelet - Neural Networks model [J]. Power System Technology, 2009, 33(17): 44-48.
[41] YANG Hong, GU Shi-fu, CUI Ming-dong, et al. Forecast of short-term wind speed in wind farms based on GA optimized LS-SVM [J]. Power System Protection and Control, 2011, 39(11): 44-48.
[42] Liu Xingjie, Mi Zengqiang, Yang Qixun, et al. Wind speed forecasting based on EMD and time-series analysis [J]. Acta Energiae Solaris Sinica, 2010, 31(8): 1037-1041.
[43] CHEN Pan, CHEN Haoyong, YE Rong, et al. Wind speed forecasting based on combination of wavelet packet analysis with support vector regression [J]. Power System Technology, 2011, 35(5): 177-182.
[44] WU Junli, ZHANG Buhan, WANG Kui. Application of Adaboost-based BP neural network for short-term wind speed forecast [J]. Power System Technology, 2012, 36(9): 221-225.
[45] WANG Yang, ZHANG Jinjiang, WEN Bojian, et al. An optimal neighborhood in phase space based local prediction method for ultra-short-term wind speed forecasting [J]. Automation of Electric Power Systems, 2012, 35(24): 39-43.
[46] ZHANG Hua, YU Yongjing , FENG Zhijun, et al. Wind speed forecasting model based on wavelet decomposition and support vector machine [J]. Journal of Hydroelectric Engineering, 2012, 31(1): 208-212.
[47] CHEN Pan, CHEN Hao-yong, YE Rong. Wind speed forecasting based on multi-scale morphological analysis [J]. Power System Protection and Control, 2010 (21): 12-18.
[48] Zheng Gao, Xiao Jian, Wang Jing, et al. Forecasting study of short-term wind speed based on interval type-2 fuzzy logic method [J]. Acta Energiae Solaris Sinica, 2012, 32(12): 1792-1797.
[49] YE Lin, LIU Peng. Combined Model Based on EMD-SVM for Short-term Wind Power Prediction [J]. Proceedings of the CSEE, 2011, 31(31): 102-108.
[50] Liu Yanan, Wei Zhinong, Zhu Yan, et al. Short-term wind speed forecast based on D-S evidence theory [J]. Electric Power Automation Equipment, 2013, 33(8): 131-136.
[51] Feng Shuanglei, Wang Weisheng, Liu Chun, et al. Short term wind speed prediction based on physical principle [J]. Acta Energiae Solaris Sinica, 2011, 32(5): 611-616.
[52] HUANG Wen-jie, FU Li, XIAO Sheng. A predictive model of wind speed based on improved fuzzy analytical hierarchy process [J]. Power System Technology, 2010, 34(7): 164-168.
[53] Lü Tao, TANG Wei, SUO Li. Prediction of short-term wind speed in wind farm based on chaotic phase space reconstruction theory [J]. Power System Protection and Control, 2010 (21): 113-117.
[54] LI Jun-fang, ZHANG Bu-han, XIE Guang-long, et al. Grey predictor models for wind speed-wind power prediction [J]. Power System Protection and Control, 2010, 38(19): 151-159.
[55] LUO Hai-yang, LIU Tian-qi, LI Xing-yuan. Chaotic forecasting method of short-term wind speed in wind farm [J]. Power System Technology, 2009, 33(9): 67-71.
[56] Ding Tao, Xiao Hongfei. Wind speed chaotic prediction model based on optimal neighborhood [J]. Acta Energiae Solaris Sinica, 2011, 32(4): 560-564.
Author Information
  • School of Economics and Management, North China Electric Power University, Changping District, Beijing, China

  • School of Economics and Management, North China Electric Power University, Changping District, Beijing, China

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  • APA Style

    Huiru Zhao, Sen Guo. (2015). Wind Speed Forecasting in China: A Review. Science Journal of Energy Engineering, 3(4-1), 14-21. https://doi.org/10.11648/j.sjee.s.2015030401.13

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    ACS Style

    Huiru Zhao; Sen Guo. Wind Speed Forecasting in China: A Review. Sci. J. Energy Eng. 2015, 3(4-1), 14-21. doi: 10.11648/j.sjee.s.2015030401.13

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    AMA Style

    Huiru Zhao, Sen Guo. Wind Speed Forecasting in China: A Review. Sci J Energy Eng. 2015;3(4-1):14-21. doi: 10.11648/j.sjee.s.2015030401.13

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  • @article{10.11648/j.sjee.s.2015030401.13,
      author = {Huiru Zhao and Sen Guo},
      title = {Wind Speed Forecasting in China: A Review},
      journal = {Science Journal of Energy Engineering},
      volume = {3},
      number = {4-1},
      pages = {14-21},
      doi = {10.11648/j.sjee.s.2015030401.13},
      url = {https://doi.org/10.11648/j.sjee.s.2015030401.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.sjee.s.2015030401.13},
      abstract = {China’s wind power has developed rapidly in the past few years, the large-scale penetration of which will bring big influence on power systems. The wind speed forecasting research is quite important because it can alleviate the negative impacts. This paper reviews the current wind speed forecasting techniques in China. The literature (written in Chinese) sources and classification were firstly analyzed, and then the wind speed forecasting techniques in China were detailed reviewed from four aspects, which are statistical method, soft computing method, hybrid forecasting method and other forecasting methods. This paper can rich the current research in the field of wind speed forecasting.},
     year = {2015}
    }
    

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  • TY  - JOUR
    T1  - Wind Speed Forecasting in China: A Review
    AU  - Huiru Zhao
    AU  - Sen Guo
    Y1  - 2015/02/10
    PY  - 2015
    N1  - https://doi.org/10.11648/j.sjee.s.2015030401.13
    DO  - 10.11648/j.sjee.s.2015030401.13
    T2  - Science Journal of Energy Engineering
    JF  - Science Journal of Energy Engineering
    JO  - Science Journal of Energy Engineering
    SP  - 14
    EP  - 21
    PB  - Science Publishing Group
    SN  - 2376-8126
    UR  - https://doi.org/10.11648/j.sjee.s.2015030401.13
    AB  - China’s wind power has developed rapidly in the past few years, the large-scale penetration of which will bring big influence on power systems. The wind speed forecasting research is quite important because it can alleviate the negative impacts. This paper reviews the current wind speed forecasting techniques in China. The literature (written in Chinese) sources and classification were firstly analyzed, and then the wind speed forecasting techniques in China were detailed reviewed from four aspects, which are statistical method, soft computing method, hybrid forecasting method and other forecasting methods. This paper can rich the current research in the field of wind speed forecasting.
    VL  - 3
    IS  - 4-1
    ER  - 

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