基于改进混沌时间序列的风电功率区间预测方法
|
摘要
风电功率区间预测是预测给定置信水平下风电功率的上限和下限,可以反映风电功率的变化范围,为调度提供有效的辅助信息。考虑风电功率的混沌特性,提出了基于改进混沌时间序列的风电功率区间预测方法。由于风电功率具有强间歇性和波动性,传统的混沌时间序列方法在风电功率区间预测中难以获得好的聚类效果和高的预测精度,影响了功率区间预测的结果。引入蚁群聚类算法和支持向量机,利用蚁群聚类算法的强搜索能力和支持向量机的强预测能力对传统方法进行改进,获得了更好的区间预测结果。将改进方法应用于英国和德国风电场的风电功率区间预测中,对比分析改进方法与基于神经网络的功率区间预测方法和传统方法在不同置信水平下的预测结果,验证了所提改进方法的有效性。
Wind power interval prediction is to predict the upper and lower limits of wind power at a given confidence level,which can reflect the variation ranges of wind power and provide effective auxiliary information for scheduling. An interval prediction method of wind power based on improved chaotic time series is proposed considering the chao-tic characteristics of wind power. Due to the strong intermittency and fluctuation of wind power,the traditional chaotic time series method is difficult to obtain good clustering effect and high prediction accuracy in wind power interval prediction,which affects the prediction results. The ant colony clustering algorithm and support vector machine are introduced to improve the traditional method by using the strong searching ability of ant colony clustering algorithm and the strong predictive ability of support vector machine,and then better interval prediction results are obtained. The improved method is applied to the wind power interval prediction of wind farms in Britain and Germany. The interval prediction results of the improved method,the interval prediction method based on neural network and the traditional method are compared and analyzed at different confidence levels,and the validity of the proposed improved method is verified.
Wind power interval prediction is to predict the upper and lower limits of wind power at a given confidence level,which can reflect the variation ranges of wind power and provide effective auxiliary information for scheduling. An interval prediction method of wind power based on improved chaotic time series is proposed considering the chao-tic characteristics of wind power. Due to the strong intermittency and fluctuation of wind power,the traditional chaotic time series method is difficult to obtain good clustering effect and high prediction accuracy in wind power interval prediction,which affects the prediction results. The ant colony clustering algorithm and support vector machine are introduced to improve the traditional method by using the strong searching ability of ant colony clustering algorithm and the strong predictive ability of support vector machine,and then better interval prediction results are obtained. The improved method is applied to the wind power interval prediction of wind farms in Britain and Germany. The interval prediction results of the improved method,the interval prediction method based on neural network and the traditional method are compared and analyzed at different confidence levels,and the validity of the proposed improved method is verified.
引文
[1] 钱政,裴岩,曹利宵,等.风电功率预测方法综述[J].高电压技术,2016,42(4):1047-1060.QIAN Zheng,PEI Yan,CAO Lixiao,et al.Review of wind power forecasting method[J].High Voltage Engineering,2016,42(4):1047-1060.
[2] 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.
[3] 黎静华,赖昌伟.考虑气象因素的短期光伏出力预测的奇异谱分析方法[J].电力自动化设备,2018,38(5):50-57,76.LI Jinghua,LAI Changwei.Singular spectrum analysis method for short-term photovoltaic output prediction considering meteorological factors[J].Electric Power Automation Equipment,2018,38(5):50-57,76.
[4] 冯双磊,王伟胜,刘纯,等.风电场功率预测物理方法研究[J].中国电机工程学报,2010,30(2):1-6.FENG Shuanglei,WANG Weisheng,LIU Chun,et al.Study on the physical approach to wind power prediction[J].Proceedings of the CSEE,2010,30(2):1-6.
[5] ERDEM E,SHI J.ARMA based approaches for forecasting the tuple of wind speed and direction[J].Applied Energy,2011,88(4):1405-1414.
[6] 冬雷,王丽婕,郝颖,等.基于自回归滑动平均模型的风力发电容量预测[J].太阳能学报,2011,32(5):617-622.DONG Lei,WANG Lijie,HAO Ying,et al.Prediction of wind power generation based on autoregressive moving average model[J].Acta Energiae Solaris Sinica,2011,32(5):617-622.
[7] EL-FOULY T H M,EL-SAADANY E F,SALAMA M M A.Impro-ved grey predictor rolling models for wind power prediction[J].IET Generation,Transmission & Distribution,2007,1(6):928-937.
[8] 杨茂,黄宾阳,江博.基于概率分布量化指标和灰色关联决策的风电功率实时预测研究[J].中国电机工程学报,2017,37(24):7099-7107.YANG Mao,HUANG Binyang,JIANG Bo.Real-time wind power prediction based on probability distribution and gray relational decision-making[J].Proceedings of the CSEE,2017,37(24):7099-7107.
[9] 潘迪夫,刘辉,李燕飞.基于时间序列分析和卡尔曼滤波算法的风电场风速预测优化模型[J].电网技术,2008,32(7):82-86.PAN Difu,LIU Hui,LI Yanfei.A wind speed forecasting optimi-zation model for wind farms based on time series analysis and Kalman filter algorithm[J].Power System Technology,2008,32(7):82-86.
[10] CHEN K L,YU J.Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach[J].Applied Energy,2014,113:690-705.
[11] KARINIOTAKIS G N,STAVRAKAKIS G S,NOGARET E F.Wind power forecasting using advanced neural networks models[J].IEEE Transactions on Energy Conversion,1996,11(4):762-767.
[12] LI G,SHI J.On comparing three artificial neural networks for wind speed forecasting[J].Applied Energy,2010,87(7):2313-2320.
[13] LEE D,BALDICK R.Short-term wind power ensemble prediction based on gaussian processes and neural networks[J].IEEE Tran-sactions on Smart Grid,2014,5(1):501-510.
[14] 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.CHENG Qiming,CHEN Lu,CHENG Yinman,et al.Short-term wind power forecasting method based on EEMD and LS-SVM model[J].Electric Power Automation Equipment,2018,38(5):27-35.
[15] ZENG J W,QIAO W.Short-term wind power prediction using a wavelet support vector machine[J].IEEE Transactions on Sustai-nable Energy,2012,3(2):255-264.
[16] TASCIKARAOGLU A,UZUNOGLU M.A review of combined approaches for prediction of short-term wind speed and power[J].Renewable & Sustainable Energy Reviews,2014,34(6):243-254.
[17] 叶林,刘鹏.基于经验模态分解和支持向量机的短期风电功率组合预测模型[J].中国电机工程学报,2011,31(31):102-108.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.
[18] LIU H,TIAN H,LIANG X,et al.New wind speed forecasting approaches using fast ensemble empirical model decomposition,genetic algorithm,mind evolutionary algorithm and artificial neural networks[J].Renewable Energy,2015,83(11):1066-1075.
[19] 黎静华,桑川川,甘一夫,等.风电功率预测技术研究综述[J].现代电力,2017,34(3):1-11.LI Jinghua,SANG Chuanchuan,GAN Yifu,et al.A review of researches on wind power forecasting technology[J].Modern Electric Power,2017,34(3):1-11.
[20] KHOSRAVI A,NAHAVANDI S,CREIGHTON D.Prediction intervals for short-term wind farm power generation forecasts[J].IEEE Transactions on Sustainable Energy,2013,4(3):602-610.
[21] KAVOUSI-FARD A,KHOSRAVI A,NAHAVANDI S.A new fuzzy-based combined prediction interval for wind power forecasting[J].IEEE Transactions on Power Systems,2016,31(1):18-26.
[22] KHOSRAVI A,NAHAVANDI S,CREIGHTON D,et al.Lower upper bound estimation method for construction of neural network-based prediction intervals[J].IEEE Transactions on Neural Networks,2011,22(3):337-346.
[23] QUAN H,SRINIVASAN D,KHOSRAVI A.Short-term load and wind power forecasting using neural network-based prediction intervals[J].IEEE Transactions on Neural Networks and Learning Systems,2014,25(2):303-315.
[24] 陈杰,沈艳霞,陆欣,等.一种风电功率概率区间多目标智能优化预测方法[J].电网技术,2016,40(8):2281-2287.CHEN Jie,SHEN Yanxia,LU Xin,et al.An intelligent multi-objective optimized method for wind power prediction intervals[J].Power System Technology,2016,40(8):2281-2287.
[25] WAN C,XU Z,PINSON P,et al.Optimal prediction intervals of wind power generation[J].IEEE Transactions on Power Systems,2014,29(3):1166-1174.
[26] 杨锡运,关文渊,刘玉奇,等.基于粒子群优化的核极限学习机模型的风电功率区间预测方法[J].中国电机工程学报,2015,35(增刊1):146-153.YANG Xiyun,GUAN Wenyuan,LIU Yuqi,et al.Prediction inter-vals forecasts of wind power based on PSO-KELM[J].Proceedings of the CSEE,2015,35(Supplement 1):146-153.
[27] ZHANG G Y,WU Y G,WONG K P,et al.An advanced approach for construction of optimal wind power prediction intervals[J].IEEE Transactions on Power Systems,2015,30(5):2706-2715.
[28] 张亚超,刘开培,秦亮,等.基于聚类经验模态分解-样本熵和优化极限学习机的风电功率多步区间预测[J].电网技术,2016,40(7):2045-2051.ZHANG Yachao,LIU Kaipei,QIN Liang,et al.Wind power multi-step interval prediction based on ensemble empirical mode decomposition-sample entropy and optimized extreme learning machine[J].Power System Technology,2016,40(7):2045-2051.
[29] HU M Y,HU Z J,YUE J P,et al.A novel multi-objective optimal approach for wind power interval prediction[J].Energies,2017,10(4):419.
[30] PINSON P,KARINIOTAKIS G.Conditional prediction intervals of wind power generation[J].IEEE Transactions on Power Systems,2010,25(4):1845-1856.
[31] 兰飞,桑川川,梁浚杰,等.基于条件Copula函数的风电功率区间预测[J].中国电机工程学报,2016,36(增刊1):79-86.LAN Fei,SANG Chuanchuan,LIANG Junjie,et al.Interval predic-tion for wind power based on conditional copula function[J].Proceedings of the CSEE,2016,36(Supplement 1):79-86.
[32] 周松林,茆美琴,苏建徽.风电功率短期预测及非参数区间估计[J].中国电机工程学报,2011,31(25):10-16.ZHOU Songlin,MAO Meiqin,SU Jianhui.Short-term forecasting of wind power and non-parametric confidence interval estimation[J].Proceedings of the CSEE,2011,31(25):10-16.
[33] 刘兴杰,谢春雨.基于贝塔分布的风电功率波动区间估计[J].电力自动化设备,2014,34(12):26-30,57.LIU Xingjie,XIE Chunyu.Wind power fluctuation interval estima-tion based on Beta distribution[J].Electric Power Automation Equipment,2014,34(12):26-30,57.
[34] 李智,韩学山,杨明,等.基于分位点回归的风电功率波动区间分析[J].电力系统自动化,2011,35(3):83-87.LI Zhi,HAN Xueshan,YANG Ming,et al.Wind power fluctuation interval analysis based on quantile regression[J].Automation of Electric Power Systems,2011,35(3):83-87.
[35] ZHANG Y C,LIU K P,QIN L,et al.Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods[J].Energy Conversion and Management,2016,112:208-219.
[36] 张学清,梁军.风电功率时间序列混沌特性分析及预测模型研究[J].物理学报,2012,61(19):70-81.ZHANG Xueqing,LIANG Jun.Chaotic characteristics analysis and prediction model study on wind power time series[J].Acta Physica Sinica,2012,61(19):70-81.
[37] 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.
[38] 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.
[39] 方仍存.电力系统负荷区间预测[D].武汉:华中科技大学,2008.FANG Rengcun.Power system load interval forecasting[D].Wu-han:Huazhong University of Science & Technology,2008.
[40] 郭杰昊.基于混沌时间序列的短期负荷预测研究[D].上海:上海交通大学,2015.GUO Jiehao.Research on short-term load algorithm for chaotic time series[D].Shanghai:Shanghai Jiaotong University,2015.
[41] 韩敏.混沌时间序列预测理论与方法[M].北京:中国水利水电出版社,2007:61-89.
[42] 裴振奎,李华,宋建伟,等.蚁群聚类算法研究及应用[J].计算机工程与设计,2008,29(19):5009-5013.PEI Zhenkui,LI Hua,SONG Jianwei,et al.Investigation and application of ant colony clustering algorithms[J].Computer Engineering and Design,2008,29(19):5009-5013.
[43] 蒋传文,袁智强,侯志俭,等.高嵌入维混沌负荷序列预测方法研究[J].电网技术,2004,28(3):25-28.JIANG Chuanwen,YUAN Zhiqiang,HOU Zhijian,et al.Research of forecasting method on chaotic load series with high embedded dimension[J].Power System Technology,2004,28(3):25-28.
[2] 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.
[3] 黎静华,赖昌伟.考虑气象因素的短期光伏出力预测的奇异谱分析方法[J].电力自动化设备,2018,38(5):50-57,76.LI Jinghua,LAI Changwei.Singular spectrum analysis method for short-term photovoltaic output prediction considering meteorological factors[J].Electric Power Automation Equipment,2018,38(5):50-57,76.
[4] 冯双磊,王伟胜,刘纯,等.风电场功率预测物理方法研究[J].中国电机工程学报,2010,30(2):1-6.FENG Shuanglei,WANG Weisheng,LIU Chun,et al.Study on the physical approach to wind power prediction[J].Proceedings of the CSEE,2010,30(2):1-6.
[5] ERDEM E,SHI J.ARMA based approaches for forecasting the tuple of wind speed and direction[J].Applied Energy,2011,88(4):1405-1414.
[6] 冬雷,王丽婕,郝颖,等.基于自回归滑动平均模型的风力发电容量预测[J].太阳能学报,2011,32(5):617-622.DONG Lei,WANG Lijie,HAO Ying,et al.Prediction of wind power generation based on autoregressive moving average model[J].Acta Energiae Solaris Sinica,2011,32(5):617-622.
[7] EL-FOULY T H M,EL-SAADANY E F,SALAMA M M A.Impro-ved grey predictor rolling models for wind power prediction[J].IET Generation,Transmission & Distribution,2007,1(6):928-937.
[8] 杨茂,黄宾阳,江博.基于概率分布量化指标和灰色关联决策的风电功率实时预测研究[J].中国电机工程学报,2017,37(24):7099-7107.YANG Mao,HUANG Binyang,JIANG Bo.Real-time wind power prediction based on probability distribution and gray relational decision-making[J].Proceedings of the CSEE,2017,37(24):7099-7107.
[9] 潘迪夫,刘辉,李燕飞.基于时间序列分析和卡尔曼滤波算法的风电场风速预测优化模型[J].电网技术,2008,32(7):82-86.PAN Difu,LIU Hui,LI Yanfei.A wind speed forecasting optimi-zation model for wind farms based on time series analysis and Kalman filter algorithm[J].Power System Technology,2008,32(7):82-86.
[10] CHEN K L,YU J.Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach[J].Applied Energy,2014,113:690-705.
[11] KARINIOTAKIS G N,STAVRAKAKIS G S,NOGARET E F.Wind power forecasting using advanced neural networks models[J].IEEE Transactions on Energy Conversion,1996,11(4):762-767.
[12] LI G,SHI J.On comparing three artificial neural networks for wind speed forecasting[J].Applied Energy,2010,87(7):2313-2320.
[13] LEE D,BALDICK R.Short-term wind power ensemble prediction based on gaussian processes and neural networks[J].IEEE Tran-sactions on Smart Grid,2014,5(1):501-510.
[14] 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.CHENG Qiming,CHEN Lu,CHENG Yinman,et al.Short-term wind power forecasting method based on EEMD and LS-SVM model[J].Electric Power Automation Equipment,2018,38(5):27-35.
[15] ZENG J W,QIAO W.Short-term wind power prediction using a wavelet support vector machine[J].IEEE Transactions on Sustai-nable Energy,2012,3(2):255-264.
[16] TASCIKARAOGLU A,UZUNOGLU M.A review of combined approaches for prediction of short-term wind speed and power[J].Renewable & Sustainable Energy Reviews,2014,34(6):243-254.
[17] 叶林,刘鹏.基于经验模态分解和支持向量机的短期风电功率组合预测模型[J].中国电机工程学报,2011,31(31):102-108.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.
[18] LIU H,TIAN H,LIANG X,et al.New wind speed forecasting approaches using fast ensemble empirical model decomposition,genetic algorithm,mind evolutionary algorithm and artificial neural networks[J].Renewable Energy,2015,83(11):1066-1075.
[19] 黎静华,桑川川,甘一夫,等.风电功率预测技术研究综述[J].现代电力,2017,34(3):1-11.LI Jinghua,SANG Chuanchuan,GAN Yifu,et al.A review of researches on wind power forecasting technology[J].Modern Electric Power,2017,34(3):1-11.
[20] KHOSRAVI A,NAHAVANDI S,CREIGHTON D.Prediction intervals for short-term wind farm power generation forecasts[J].IEEE Transactions on Sustainable Energy,2013,4(3):602-610.
[21] KAVOUSI-FARD A,KHOSRAVI A,NAHAVANDI S.A new fuzzy-based combined prediction interval for wind power forecasting[J].IEEE Transactions on Power Systems,2016,31(1):18-26.
[22] KHOSRAVI A,NAHAVANDI S,CREIGHTON D,et al.Lower upper bound estimation method for construction of neural network-based prediction intervals[J].IEEE Transactions on Neural Networks,2011,22(3):337-346.
[23] QUAN H,SRINIVASAN D,KHOSRAVI A.Short-term load and wind power forecasting using neural network-based prediction intervals[J].IEEE Transactions on Neural Networks and Learning Systems,2014,25(2):303-315.
[24] 陈杰,沈艳霞,陆欣,等.一种风电功率概率区间多目标智能优化预测方法[J].电网技术,2016,40(8):2281-2287.CHEN Jie,SHEN Yanxia,LU Xin,et al.An intelligent multi-objective optimized method for wind power prediction intervals[J].Power System Technology,2016,40(8):2281-2287.
[25] WAN C,XU Z,PINSON P,et al.Optimal prediction intervals of wind power generation[J].IEEE Transactions on Power Systems,2014,29(3):1166-1174.
[26] 杨锡运,关文渊,刘玉奇,等.基于粒子群优化的核极限学习机模型的风电功率区间预测方法[J].中国电机工程学报,2015,35(增刊1):146-153.YANG Xiyun,GUAN Wenyuan,LIU Yuqi,et al.Prediction inter-vals forecasts of wind power based on PSO-KELM[J].Proceedings of the CSEE,2015,35(Supplement 1):146-153.
[27] ZHANG G Y,WU Y G,WONG K P,et al.An advanced approach for construction of optimal wind power prediction intervals[J].IEEE Transactions on Power Systems,2015,30(5):2706-2715.
[28] 张亚超,刘开培,秦亮,等.基于聚类经验模态分解-样本熵和优化极限学习机的风电功率多步区间预测[J].电网技术,2016,40(7):2045-2051.ZHANG Yachao,LIU Kaipei,QIN Liang,et al.Wind power multi-step interval prediction based on ensemble empirical mode decomposition-sample entropy and optimized extreme learning machine[J].Power System Technology,2016,40(7):2045-2051.
[29] HU M Y,HU Z J,YUE J P,et al.A novel multi-objective optimal approach for wind power interval prediction[J].Energies,2017,10(4):419.
[30] PINSON P,KARINIOTAKIS G.Conditional prediction intervals of wind power generation[J].IEEE Transactions on Power Systems,2010,25(4):1845-1856.
[31] 兰飞,桑川川,梁浚杰,等.基于条件Copula函数的风电功率区间预测[J].中国电机工程学报,2016,36(增刊1):79-86.LAN Fei,SANG Chuanchuan,LIANG Junjie,et al.Interval predic-tion for wind power based on conditional copula function[J].Proceedings of the CSEE,2016,36(Supplement 1):79-86.
[32] 周松林,茆美琴,苏建徽.风电功率短期预测及非参数区间估计[J].中国电机工程学报,2011,31(25):10-16.ZHOU Songlin,MAO Meiqin,SU Jianhui.Short-term forecasting of wind power and non-parametric confidence interval estimation[J].Proceedings of the CSEE,2011,31(25):10-16.
[33] 刘兴杰,谢春雨.基于贝塔分布的风电功率波动区间估计[J].电力自动化设备,2014,34(12):26-30,57.LIU Xingjie,XIE Chunyu.Wind power fluctuation interval estima-tion based on Beta distribution[J].Electric Power Automation Equipment,2014,34(12):26-30,57.
[34] 李智,韩学山,杨明,等.基于分位点回归的风电功率波动区间分析[J].电力系统自动化,2011,35(3):83-87.LI Zhi,HAN Xueshan,YANG Ming,et al.Wind power fluctuation interval analysis based on quantile regression[J].Automation of Electric Power Systems,2011,35(3):83-87.
[35] ZHANG Y C,LIU K P,QIN L,et al.Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods[J].Energy Conversion and Management,2016,112:208-219.
[36] 张学清,梁军.风电功率时间序列混沌特性分析及预测模型研究[J].物理学报,2012,61(19):70-81.ZHANG Xueqing,LIANG Jun.Chaotic characteristics analysis and prediction model study on wind power time series[J].Acta Physica Sinica,2012,61(19):70-81.
[37] 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.
[38] 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.
[39] 方仍存.电力系统负荷区间预测[D].武汉:华中科技大学,2008.FANG Rengcun.Power system load interval forecasting[D].Wu-han:Huazhong University of Science & Technology,2008.
[40] 郭杰昊.基于混沌时间序列的短期负荷预测研究[D].上海:上海交通大学,2015.GUO Jiehao.Research on short-term load algorithm for chaotic time series[D].Shanghai:Shanghai Jiaotong University,2015.
[41] 韩敏.混沌时间序列预测理论与方法[M].北京:中国水利水电出版社,2007:61-89.
[42] 裴振奎,李华,宋建伟,等.蚁群聚类算法研究及应用[J].计算机工程与设计,2008,29(19):5009-5013.PEI Zhenkui,LI Hua,SONG Jianwei,et al.Investigation and application of ant colony clustering algorithms[J].Computer Engineering and Design,2008,29(19):5009-5013.
[43] 蒋传文,袁智强,侯志俭,等.高嵌入维混沌负荷序列预测方法研究[J].电网技术,2004,28(3):25-28.JIANG Chuanwen,YUAN Zhiqiang,HOU Zhijian,et al.Research of forecasting method on chaotic load series with high embedded dimension[J].Power System Technology,2004,28(3):25-28.