采用k近邻进行空间相关性超短期风速预测
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摘要
提高超短期风速预测准确率和可靠性的途径之一,是从历史观测值中充分挖掘风速相关性的特征和规律。将本地最新的风速历史观测值结合按照最优延迟时间提前的上游风速观测值,形成空间相关性k近邻预测的参考矢量;以相关系数作为相关性的具体评价指标,从风速历史观测值中优选出该参考矢量的k个最相似的近邻;采用7种回归模型进行本地的未来风速预测。荷兰Huibertgat地区冬季风速预测的仿真结果表明:使用线性回归、偏最小二乘回归、最小二乘支持向量回归3个优化模型预测,优化的k近邻数量为100左右,优化的历史数据年数为10 a;空间相关性k近邻风速预测能够有效使用历史数据的相似性进行可靠的超短期风速预测。
One approach to improve the accuracy and reliability of ultra-short term wind speed prediction is to thoroughly exploit the characteristics and laws of wind speed correlation from historical observations. The reference vector of k-nearest neighbor prediction based on spatial correlation is formed by the combination of latest local wind speed historical observations and upstream wind speed observations adjusted by its optimal lag time. The correlation coefficient is taken as the concrete evaluation index,and k most similar neighbours of the reference vector are optimally selected from the wind speed historical observations. Seven regression models are adopted for the future local wind speed prediction. The simulative results of wind speed prediction of Huibertgat,Holland in winter show that the optimal number of k-nearest neighbours is about 100 and the optimal year number of historical data is 10 a by the prediction of three optimal models,i.e. linear regression,partial least squares regression and least squares support vector machine regression,and the proposed method can effectively use the similarity of historical data for reliable ultrashort term wind speed prediction.
One approach to improve the accuracy and reliability of ultra-short term wind speed prediction is to thoroughly exploit the characteristics and laws of wind speed correlation from historical observations. The reference vector of k-nearest neighbor prediction based on spatial correlation is formed by the combination of latest local wind speed historical observations and upstream wind speed observations adjusted by its optimal lag time. The correlation coefficient is taken as the concrete evaluation index,and k most similar neighbours of the reference vector are optimally selected from the wind speed historical observations. Seven regression models are adopted for the future local wind speed prediction. The simulative results of wind speed prediction of Huibertgat,Holland in winter show that the optimal number of k-nearest neighbours is about 100 and the optimal year number of historical data is 10 a by the prediction of three optimal models,i.e. linear regression,partial least squares regression and least squares support vector machine regression,and the proposed method can effectively use the similarity of historical data for reliable ultrashort term wind speed prediction.
引文
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[20]LI X,HU F,LIU G. Characteristics of chaotic attractors in atmospheric boundary-layer turbulence[J]. Boundary-Layer Meteorology,2001,99(2):335-345.
[2]乔颖,鲁宗相,闵勇.提高风电功率预测精度的方法[J].电网技术,2017,41(10):3261-3268.QIAO Ying,LU Zongxiang,MIN Yong. Research&application of raising wind power prediction accuracy[J]. Power System Technology,2017,41(10):3261-3268.
[3]杨茂,杨琼琼.风电机组风速-功率特性曲线建模研究综述[J].电力自动化设备,2018,38(2):34-43.YANG Mao,YANG Qiongqiong. Review of modeling of wind speedpower characteristic curve for wind turbine[J]. Electric Power Automation Equipment,2018,38(2):34-43.
[4]王兰,李华强,吴星,等.基于改进局域Volterra自适应滤波器的风电功率混沌时间序列预测模型[J].电力自动化设备,2016,36(8):40-44.WANG Lan,LI Huaqiang,WU Xing,et al. Wind power chaotic time series prediction model based on improved local Volterra adaptive filter[J]. Electric Power Automation Equipment,2016,36(8):40-44.
[5]范磊,卫志农,李慧杰,等.基于变分模态分解和蝙蝠算法-相关向量机的短期风速区间预测[J].电力自动化设备,2017,37(1):93-100.FAN Lei,WEI Zhinong,LI Huijie,et al. Short-term wind speed interval prediction based on VMD and BA-RVM algorithm[J].Electric Power Automation Equipment,2017,37(1):93-100.
[6]欧阳庭辉,查晓明,秦亮,等.含核函数切换的风电功率短期预测新方法[J].电力自动化设备,2016,36(9):80-86.OUYANG Tinghui,ZHA Xiaoming,QIN Liang,et al. Short-term wind power prediction based on kernel function switching[J]. Electric Power Automation Equipment,2016,36(9):80-86.
[7]李军,常燕芝.基于KPCA-KMPMR的短期风电功率概率预测[J].电力自动化设备,2017,37(2):22-28.LI Jun,CHANG Yanzhi. Short-term probabilistic forecasting based on KPCA-KMPMR for wind power[J]. Electric Power Automation Equipment,2017,37(2):22-28.
[8]JUNG J,BROADWATER R P. Current status and future advances for wind speed and power forecasting[J]. Renewable and Sustainable Energy Reviews,2014(31):762-777.
[9]叶林,赵永宁.基于空间相关性的风电功率预测研究综述[J].电力系统自动化,2014,38(14):126-135.YE Lin,ZHAO Yongning. A review on wind power prediction based on spatial correlation approach[J]. Automation of Electric Power Systems,2014,38(14):126-135.
[10]YE L,ZHAO Y,ZENG C,et al. Short-term wind power prediction based on spatial model[J]. Renewable Energy,2017(101):1067-1074.
[11]陈宁,薛禹胜,丁杰,等.利用空间相关性的超短期风速预测[J].电力系统自动化,2017,41(12):124-130.CHEN Ning,XUE Yusheng,DING Jie,et al. Ultra-short term wind speed prediction using spatial correlation[J]. Automation of Electric Power Systems,2017,41(12):124-130.
[12]杨正瓴,杨钊,张玺,等.基于季风提高空间相关性预测的优化延迟时间[J].电力系统保护与控制,2016,44(15):33-38.YANG Zhengling,YANG Zhao,ZHANG Xi,et al. Improving optimal lag time of spatial correlation prediction by characteristics of monsoon[J]. Power System Protection and Control,2016,44(15):33-38.
[13]ZHANG Y,WANG J. K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting[J].International Journal of Forecasting,2016,32(3):1074-1080.
[14] COVER T,HART P. Nearest neighbor pattern classification[J].IEEE Transactions on Information Theory,1967,13(1):21-27.
[15] SKRIMPAS G A,KLEANI K,MIJATOVIC N,et al. Detection of icing on wind turbine blades by means of vibration and power curve analysis[J]. Wind Energy,2016,19(10):1819-1832.
[16] EFRON B,HASTIE T. Computer age statistical inference[M].Cambridge,UK:Cambridge University Press,2016.
[17]ZHAO J,GUO Z,SU Z,et al. An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed[J]. Applied Energy,2016,162:808-826.
[18]杨正瓴,刘阳,张泽,等.采用最近历史观测值和PLSR进行空间相关性超短期风速预测[J].电网技术,2017,41(6):1815-1822.YANG Zhengling,LIU Yang,ZHANG Ze,et al. Ultra-short-term wind speed prediction with spatial correlation using recent historical observations and PLSR[J]. Power System Technology,2017,41(6):1815-1822.
[19]HAGSPIEL S,PAPAEMANNOUIL A,SCHMID M,et al. Copulabased modeling of stochastic wind power in Europe and implications for the Swiss Power Grid[J]. Applied Energy,2012,96:33-44.
[20]LI X,HU F,LIU G. Characteristics of chaotic attractors in atmospheric boundary-layer turbulence[J]. Boundary-Layer Meteorology,2001,99(2):335-345.