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基于混核LSSVM的批特征风功率预测方法

刘畅 郎劲

刘畅, 郎劲. 基于混核LSSVM的批特征风功率预测方法. 自动化学报, 2020, 46(6): 1264-1273. doi: 10.16383/j.aas.c180103
引用本文: 刘畅, 郎劲. 基于混核LSSVM的批特征风功率预测方法. 自动化学报, 2020, 46(6): 1264-1273. doi: 10.16383/j.aas.c180103
LIU Chang, LANG Jin. Wind Power Prediction Method Using Hybrid Kernel LSSVM With Batch Feature. ACTA AUTOMATICA SINICA, 2020, 46(6): 1264-1273. doi: 10.16383/j.aas.c180103
Citation: LIU Chang, LANG Jin. Wind Power Prediction Method Using Hybrid Kernel LSSVM With Batch Feature. ACTA AUTOMATICA SINICA, 2020, 46(6): 1264-1273. doi: 10.16383/j.aas.c180103

基于混核LSSVM的批特征风功率预测方法

doi: 10.16383/j.aas.c180103
基金项目: 

国家重点研究发展计划基金 2016yfb0901900

国家自然科学基金重点国际合作项目 71520107004

流程工业综合自动化国家重点实验室基础研究项目基金 2013zcx02

111引智基地基金 b16009

详细信息
    作者简介:

    郎劲  东北大学辽宁省制造系统与物流优化重点实验室、东北大学流程工业综合自动化国家重点实验室讲师.主要研究方向为可再生能源, 风力发电和电力系统. E-mail: langjin@ise.neu.edu.cn

    通讯作者:

    刘畅  智能工业数据解析与优化教育部重点实验室(东北大学)、东北大学工业与系统工程研究所博士研究生.主要研究方向为机器学习和智能优化.本文通信作者. E-mail: lc1987328@126.com

Wind Power Prediction Method Using Hybrid Kernel LSSVM With Batch Feature

Funds: 

national key research and development program of china 2016yfb0901900

the major international joint research project of national natural science foundation of china 71520107004

the state key laboratory of synthetical automation for process industrial fundamental research funds 2013zcx02

the 111 project b16009

More Information
    Author Bio:

    LANG Jin  Lecturer at Liaoning Key Laboratory of Manufacturing System and Logistics, and the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. Her research interest covers renewable energy, wind power, and power systems

    Corresponding author: LIU Chang  Ph. D. candidate at the Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, and the Institute of Industrial and Systems Engineering, Northeastern University. His research interest covers machine learning and intelligent optimization. Corresponding author of this paper
  • 摘要: 针对风电场风功率预测问题, 利用历史风功率、气象数据和测风塔实时数据等相关信息, 提出了带有批特征的混核最小二乘支持向量机(Hybrid kernel least squares support vector machine, HKLSSVM)方法, 建立风电场风功率预测模型.为了增强模型的适应性, 设计改进的差分进化算法对模型参数进行优化, 并利用稀疏选择方法来选取合适的训练样本集, 缩短建模时间, 保证预测模型精度.根据风场风机的地理位置分布情况, 提出批划分的建模策略, 对相近地理位置的风机进行组批, 替代传统风场风功率预测方法.通过风场中实际数据进行测试, 实验结果表明与其他预测方法相比, 本文提出的方法能够提高预测精度和效率, 减少风电波动性对电网的影响, 从而提高电网的安全性和可靠性.
    Recommended by Associate Editor SUN Qiu-Ye
    1)  本文责任编委 孙秋野
  • 图  1  BSIDE-HKLSSVM流程图

    Fig.  1  The flowchart of BSIDE-HKLSSVM

    图  2  某风场的风机分布

    Fig.  2  The distribution of wind turbines in the wind farm

    图  3  IDE与DE的风速预测模型收敛比较

    Fig.  3  The convergence comparison of wind speed prediction model between IDE and DE

    图  4  IDE与DE的风功率预测模型收敛比较

    Fig.  4  The convergence comparison of wind power prediction model between IDE and DE

    图  5  风场风速预测曲线

    Fig.  5  The prediction curve of wind speed in the wind farm

    图  6  风场风功率预测曲线

    Fig.  6  The prediction curve of wind power in the wind farm

    图  7  风速预测误差分布

    Fig.  7  The distribution of wind speed prediction errors

    图  8  风功率预测误差分布

    Fig.  8  The distribution of wind power prediction errors

    表  1  IDE的参数设置

    Table  1  Parameters setting of IDE

    参数${NP}$${M}$${g_{\max }}$$\theta$$CR$${F_1}$
    取值100420100.70.3
    下载: 导出CSV

    表  2  BSIDE-HKLSSVM的参数设置范围

    Table  2  Parameters setting scope of BSIDE-HKLSSVM

    参数$\lambda$$\sigma$$\gamma$${d}$${a_1}$
    范围[0.05, 0.1][1,50](0, 1 000][1,10](0, 0.0001]
    下载: 导出CSV

    表  3  风场风速预测结果

    Table  3  Prediction results of wind speed in the wind farm

    方法RMSEMAXE (m/s)ARE
    SIDE-HKLSSVM0.612.070.07
    IDE-HKLSSVM0.832.160.10
    IDE-LSSVM1.143.170.12
    SVR2.143.760.25
    ELM1.212.350.13
    下载: 导出CSV

    表  4  基于BSIDE-HKLSSVM方法的批样机风功率预测结果

    Table  4  Prediction results of wind power based on BSIDE-HKLSSVM method for batch turbines

    批样机RMSEMAXE (MW)ARE
    9#0.180.630.35
    18#0.150.610.42
    31#0.120.460.51
    37#0.140.500.70
    52#0.130.530.90
    59#0.120.630.44
    下载: 导出CSV

    表  5  基于BIDE-HKLSSVM方法的批样机风功率预测结果

    Table  5  Prediction results of wind power based on BIDE-HKLSSVM method for batch turbines

    批样机RMSEMAXE (MW)ARE
    9#0.160.600.32
    18#0.140.620.36
    31#0.180.440.92
    37#0.120.510.50
    52#0.130.510.94
    59#0.120.640.41
    下载: 导出CSV

    表  6  风场风功率预测结果

    Table  6  Prediction results of wind power in the wind farm

    方法RMSEMAXE (MW)AREPAR (%)
    BSIDE-HKLSSVM 4.57 17.36 0.21 95.4
    BIDE-HKLSSVM4.8418.860.2295.1
    SIDE-HKLSSVM6.7921.940.2293.1
    IDE-HKLSSVM7.4425.98 0.2192.5
    IDE-LSSVM8.3526.950.2691.6
    STS8.0922.010.3391.8
    SVR7.5327.340.2292.4
    ELM7.9327.170.3392.0
    EE13.0836.050.3386.8
    下载: 导出CSV

    表  7  带有稀疏策略的风场风功率训练模型时间比较

    Table  7  Time comparisons of training model with sparsity strategy for the wind power in the wind farm

    方法时间(s)
    BSIDE-HKLSSVM382.27
    BIDE-HKLSSVM474.07
    SIDE-HKLSSVM 40.71
    IDE-HKLSSVM64.93
    下载: 导出CSV
  • [1] MaríL, Nabona N. Renewable energies in medium-term power planning. IEEE Transactions on Power Systems, 2015, 30(1): 88-97 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=e8fff6a927f62a4ded3acf054ee093cf
    [2] 郎劲, 唐立新.考虑风力发电批特征的电力机组调度问题.自动化学报, 2015, 41(7): 1295-1305 doi: 10.16383/j.aas.2015.c140503

    Lang Jin, Tang Li-Xin. Unit commitment problem for wind turbines power generation with batching characteristics consideration. Acta Automatica Sinica, 2015, 41(7): 1295- 1305 doi: 10.16383/j.aas.2015.c140503
    [3] Moeini-Aghtaie M, Farzin H, Fotuhi-Firuzabad M, Amrollahi R. Generalized analytical approach to assess reliability of renewable-based energy hubs. IEEE Transactions on Power Systems, 2017, 32(1): 368-377 http://ieeexplore.ieee.org/document/7457314/
    [4] Tascikaraoglu A, Uzunoglu M. A review of combined approaches for prediction of short-term wind speed and power. Renewable and Sustainable Energy Reviews, 2014, 34: 243 -254 doi: 10.1016/j.rser.2014.03.033
    [5] Croonenbroeck C, Ambach D. A selection of time series models for short- to medium-term wind power forecasting. Journal of Wind Engineering and Industrial Aerodynamics, 2015, 136: 201-210 doi: 10.1016/j.jweia.2014.11.014
    [6] Colak I, Sagiroglu S, Yesilbudak M. Data mining and wind power prediction: A literature review. Renewable Energy, 2012, 46: 241-247 doi: 10.1016/j.renene.2012.02.015
    [7] Yuan X H, Tan Q X, Lei X H, Yuan Y B, Wu X T. Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine. Energy, 2017, 129: 122-137 doi: 10.1016/j.energy.2017.04.094
    [8] Wan C, Xu Z, Pinson P, Dong Z Y, Wong K P. Probabilistic forecasting of wind power generation using extreme learning machine. IEEE Transactions on Power Systems, 2014, 29(3): 1033-1044 https://ieeexplore.ieee.org/document/6665108/
    [9] Qureshi A S, Khan A, Zameer A, Usman A. Wind power prediction using deep neural network based meta regression and transfer learning. Applied Soft Computing, 2017, 58: 742-755 doi: 10.1016/j.asoc.2017.05.031
    [10] Pousinho H M I, Mendes V M F, Catalão J P S. A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal. Energy Conversion and Management, 2011, 52: 397-402 doi: 10.1016/j.enconman.2010.07.015
    [11] Haque A U, Mandal P, Meng J L, Srivastava A K, Tseng T L, Senjyu T. A novel hybrid approach based on wavelet transform and fuzzy ARTMAP networks for predicting wind farm power production. IEEE Transactions on Industry Applications, 2013, 49(5): 2253-2261 doi: 10.1109/TIA.2013.2262452
    [12] Heinermann J, Kramer O. Machine learning ensembles for wind power prediction. Renewable Energy, 2016, 89: 671- 679 doi: 10.1016/j.renene.2015.11.073
    [13] Ye L, Zhao Y N, Zeng C, Zhang C H. Short-term wind power prediction based on spatial model. Renewable Energy, 2017, 101: 1067-1074 doi: 10.1016/j.renene.2016.09.069
    [14] 刘强, 秦泗钊.过程工业大数据建模研究展望.自动化学报, 2016, 42(2): 161-171 doi: 10.16383/j.aas.2015.c140503

    Liu Qiang, Qin S Joe. Perspectives on big data modeling of process industries. Acta Automatica Sinica, 2016, 42(2): 161-171 doi: 10.16383/j.aas.2015.c140503
    [15] 陶剑文, 王士同.领域适应核支持向量机.自动化学报, 2012, 38(5): 797-811 doi: 10.3724/SP.J.1004.2012.00797

    Tao Jian-Wen, Wang Shi-Tong. Kernel support vector machine for domain adaptation. Acta Automatica Sinica, 2012, 38(5): 797-811 doi: 10.3724/SP.J.1004.2012.00797
    [16] 石勇, 李佩佳, 汪华东. L2损失大规模线性非平行支持向量顺序回归模型.自动化学报, 2019, 45(3): 505-517 doi: 10.16383/j.aas.2018.c170438

    Shi Yong, Li Pei-Jia, Wang Hua-Dong. L2-Loss large-scale linear nonparallel support vector ordinal regression. Acta Automatica Sinica, 2019, 45(3): 505-517 doi: 10.16383/j.aas.2018.c170438
    [17] 高明哲, 许爱强, 唐小峰, 张伟.基于多核多分类相关向量机的模拟电路故障诊断方法.自动化学报, 2019, 45(2): 434-444 doi: 10.16383/j.aas.2017.c160779

    Gao Ming-Zhe, Xu Ai-Qiang, Tang Xiao-Feng, Zhang Wei. Analog circuit diagnostics method based on multi-kernel learning multiclass relevance vector machine. Acta Automatica Sinica, 2019, 45(2): 434-444 doi: 10.16383/j.aas.2017.c160779
    [18] 张凯军, 梁循.一种改进的显性多核支持向量机.自动化学报, 2014, 40(10): 2288-2294 doi: 10.3724/SP.J.1004.2014.02288

    Zhang Kai-Jun, Liang Xun. An improved domain multiple kernel support vector machine. Acta Automatica Sinica, 2014, 40(10): 2288-2294 doi: 10.3724/SP.J.1004.2014.02288
    [19] Ouyang T H, Zha X M, Qin L, Xiong Y, Xia T. Wind power prediction method based on regime of switching kernel functions. Journal of Wind Engineering and Industrial Aerodynamics, 2016, 153: 26-33 doi: 10.1016/j.jweia.2016.03.005
    [20] Ouyang T H, Zha X M, Qin L. A combined multivariate model for wind power prediction. Energy Conversion and Management, 2017, 144: 361-373 doi: 10.1016/j.enconman.2017.04.077
    [21] Khosravi A, Koury R N N, Machado L, Pabon J J G. Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system. Sustainable Energy Technologies and Assessments, 2018, 25: 146-160 doi: 10.1016/j.seta.2018.01.001
    [22] Yuan X H, Chen C, Yuan Y B, Huang Y H, Tan Q X. Short-term wind power prediction based on LSSVM-GSA model. Energy Conversion and Management, 2015, 101: 393-401 doi: 10.1016/j.enconman.2015.05.065
    [23] Zhang L L, Li M S, Ji T Y, Wu Q H. Short-term wind power prediction based on intrinsic time-scale decomposition and LS-SVM. In: Proceedings of the 2016 IEEE Innovative Smart Grid Technologies- Asia (ISGT-Asia). Melbourne, Australia: IEEE, 2016. 41-45
    [24] Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer Verlag, 1995.
    [25] Liu C, Tang L X, Liu J Y, Tang Z H. A dynamic analytics method based on multistage modeling for a BOF steelmaking process. IEEE Transactions on Automation Science and Engineering, 2019, 16(3): 1097-1109 doi: 10.1109/TASE.2018.2865414
    [26] Storn R, Price K. Differential Evolution-A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces, Technical Report TR-95-012, International Computer Science Institute, Berkeley, USA, 1995.
    [27] Suykens J A K, van Gestel T, de Brabanter J, de Moor B, Vandewalle J. Least Squares Support Vector Machines. Singapore: World Scientific, 2003.
    [28] Drucker H, Burges C J, Kaufman L, Smola A J, Vapnik V. Support vector regression machines. In: Proceedings of the 1997 Advances in Neural Information Processing Systems. Denver, CO, USA: NIPS, 1997. 155-161
    [29] Huang G B, Zhou H M, Ding X J, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012, 42(2): 513-529 doi: 10.1109/TSMCB.2011.2168604
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  • 收稿日期:  2018-02-26
  • 录用日期:  2018-05-07
  • 刊出日期:  2020-07-10

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