基于多重离群点平滑转换自回归模型的短期风电功率预测
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摘要
基于对风电功率时间序列波动性多重机制的研究,提出一种基于多重离群点平滑转换自回归模型(M-OSTAR)的风电功率预测方法。运用一种改进条件极大似然估计方法,获得M-OSTAR模型的参数估计。考虑风电波动性的厚尾效应,将M-OSTAR模型推广为厚尾形式。进一步借助所提模型的机制转换参数,描述了风电时间序列的多重离群点效应。此外,给出了一种新型的波动性分析工具——标准信息冲击曲面,分析了风电时间序列条件方差的动态变化特征。基于实际风电数据的算例验证了基于M-OSTAR族模型预测方法的可行性与有效性。
Based on the analysis on the different regimes in the volatility of wind power time series, a prospective wind power forecasting method based on Multiple Outlier Smooth Transition Autoregressive(MOSTAR) type models is presented. By modifying Conditional Maximum Likelihood Estimate(CMLE), the parameters of the MOSTAR models are obtained. Considering the fat-tail effect in the volatility of wind power time series, MOSTAR models with fat-tail distribution are proposed for generalization. Moreover, with the regime switching parameter of the proposed model, the multiple outlier effect of real case is depicted more rigorously. In addition, Standard News Impact Surface(SNIS), a refined tool for volatility analysis is provided to analyze the dynamic varying characteristics of conditional variance. Case studies on a practical wind power data validate the feasibility and effectiveness of M-OSTAR type model.
Based on the analysis on the different regimes in the volatility of wind power time series, a prospective wind power forecasting method based on Multiple Outlier Smooth Transition Autoregressive(MOSTAR) type models is presented. By modifying Conditional Maximum Likelihood Estimate(CMLE), the parameters of the MOSTAR models are obtained. Considering the fat-tail effect in the volatility of wind power time series, MOSTAR models with fat-tail distribution are proposed for generalization. Moreover, with the regime switching parameter of the proposed model, the multiple outlier effect of real case is depicted more rigorously. In addition, Standard News Impact Surface(SNIS), a refined tool for volatility analysis is provided to analyze the dynamic varying characteristics of conditional variance. Case studies on a practical wind power data validate the feasibility and effectiveness of M-OSTAR type model.
引文
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[17]陈昊.采用现代时间序列分析方法的电力负荷预测[M].北京:中国电力出版社,2015:139-142.
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[19]ENGLE R,NG V.Measuring and testing the impact of news on volatility[J].Journal of Finance,1993,48(5):1749-1778.
[2]GWEC.Global wind report annual market[EB/OL].http://gwec.net/publications/global-wind-report-2/global-windreport-2016/2017-4-28/2017-12-25.
[3]乔颖,鲁宗相,闵勇,等.提高风电功率预测精度的方法[J].电网技术,2017,41(10):3161-3169.QIAO Ying,LU Zongxiang,MIN Yong,et al.Research&application of raising wind power prediction accuracy[J].Power System Technology,2017,41(10):3161-3169.
[4]杨正瓴,冯勇,熊定方,等.基于季风特性改进风电功率预测的研究展望[J].智能电网,2015,3(1):1-7.YANG Zhengling,FENG Yong,XIONG Dingfang,et al.Research of prospects of improvement in wind power forecasting based on characteristics of monsoons[J].Smart Grid,2015,3(1):1-7.
[5]高阳,朴在林,张旭鹏,等.基于噪声场合下ARMA模型的风力发电量预测[J].电力系统保护与控制,2010,38(20):164-167.GAO Yang,PIAO Zailin,ZHANG Xupeng,et al.Prediction of wind power generation based on ARMA with additive noise model[J].Power System Protection and Control,2010,38(20):164-167.
[6]陈昊,万秋兰,王玉荣.基于厚尾均值广义自回归条件异方差族模型的短期风电功率预测[J].电工技术学报,2016,31(5):91-98.CHEN Hao,WAN Qiulan,WANG Yurong.Short-term wind power forecast based on fat-tailed generalized autoregressive conditional heteroscedasticity-in-mean type models[J].Transactions of China Electrotechnical Society,2016,31(5):91-98.
[7]CHEN H,LI F,WANG Y.Wind power forecasting based on outlier smooth transition autoregressive GARCHmodel[J].Journal of Modern Power Systems and Clean Energy,2018,6(3):532-539.
[8]江岳春,张丙江,邢方方,等.基于混沌时间序列GA-VNN模型的超短期风功率多步预测[J].电网技术,2015,39(8):2160-2166.JIANG Yuechun,ZHANG Bingjiang,XING Fangfang,et al.Super-short-term multi-step prediction of wind power based on GA-VNN model of chaotic time series[J].Power System Technology,2015,39(8):2160-2166.
[9]马斌,张丽艳.一种基于径向基神经网络的短期风电功率直接预测方法[J].电力系统保护与控制,2015,43(19):78-82.MA Bin,ZHANG Liyan.Short-term wind power direct forecasting based on RBF neural network[J].Power System Protection and Control,2015,43(19):78-82.
[10]ZHENG Dehua,ESEYE A T,ZHANG Jianhua,et al.Short-term wind power forecasting using adouble-stage hierarchical ANFIS approach for energy management in microgrids[J].Protection and Control of Modern Power Systems,2017,2(2):136-145.DOI:10.1186/s41601-017-0041-5.
[11]李燕青,袁燕舞,郭通.基于AMD-ICSA-SVM的超短期风电功率组合预测[J].电力系统保护与控制,2017,45(14):113-120.LI Yanqing,YUAN Yanwu,GUO Tong.Combination ultra-short-term prediction of wind power based on AMD-ICSA-SVM[J].Power System Protection and Control,2017,45(14):113-120.
[12]张颖超,郭晓杰,叶小岭,等.一种短期风电功率集成预测方法[J].电力系统保护与控制,2016,44(7):90-95.ZHANG Yingchao,GUO Xiaojie,YE Xiaoling,et al.An integrated forecasting method of short-term wind power[J].Power System Protection and Control,2016,44(7):90-95.
[13]薛禹胜,陈宁,王树民,等.关于利用空间相关性预测风速的评述[J].电力系统自动化,2017,41(10):161-169.XUE Yusheng,CHEN Ning,WANG Shumin,et al.Areview on wind speed prediction based on spatial correlation[J].Automation of Electric Power Systems,2017,41(10):161-169.
[14]TER?SVIRTA T,ANDERSON H M.Characterizing nonlinearities in business cycles using Smooth Transition Autoregressive models[J].Journal of Applied Econometrics,1992,7(12):119-136.
[15]ENGLE R F.Autoregressive conditional heteroskedasticity with estimate of the variance of U.K.inflation[J].Econometrica,1982,50(4):987-1008.
[16]BOLLERSLEV T.Generalized autoregressive conditional heteroskedasticity[J].Journal of Econometrics,1986,31(3):307-327.
[17]陈昊.采用现代时间序列分析方法的电力负荷预测[M].北京:中国电力出版社,2015:139-142.
[18]BERNDT E,HALL B,HALL R,et al.Estimation and inference in nonlinear structural models[J].Annals of Economic and Social Measurement,1974,3(4):653-665.
[19]ENGLE R,NG V.Measuring and testing the impact of news on volatility[J].Journal of Finance,1993,48(5):1749-1778.
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