廖旎焕,胡智宏,马莹莹,等．电力系统短期负荷预测方法综述[J]．电力系统保护与控制,2011,39(1)：147-152．Liao Nihuan,Hu Zhihong,Ma Yingying,et al．Review of the short-term load forecasting methods of electric power system[J]．Power System Protection and Control,2011,39(1)：147-152(in Chinese)．
[2]
刘国徽,刘小满,余雪芳,等．基于ARIMA和LS-SVM组合模型的短期负荷预测[J]．广东电力,2010,23(11)：14-17．Liu Guohui,Liu Xiaoman,Yu Xuefang,et al．Short-term load forecasting based of ARIMA-LSVM Model[J]．Guangdong Electric Power,2010,23(11)：14-17(in Chinese)．
[3]
梁志珊,王丽敏,付大鹏,等．基于Lyapunov指数的电力系统短期负荷预测[J]．中国电机工程学报,1998,18(5)：368-371,376．Liang Zhishan,Wang Liming,Fu DaPeng,et al．Short-time load forecasting based on the maximum lyapunov exponent[J]．Proceedings of the CSEE,1998,18(5)：368-371,376 (in Chinese)．
[4]
何洋,邹波,李文启,等．基于混沌理论的电力系统短期负荷预测的局域模型[J]．华北电力大学学报(自然科学版),2013,40(4)：43-50．He Yang,Zou Bo,Li Wenqi,et al．A chaos theory based local model for short-term load forecasting[J]．Journal of North China Electric Power University (Natural Science Edition),2013,40(4)：43-50(in Chinese)．
[5]
祖哲,毕贵红,刘力．基于混沌理论的电力系统短期负荷预测模型研究[C]// 2012 International Conference on Electronic Information and Electrical Engineering．Changsha,Hunan,China：Atlantis Press,2012：904-911．
[6]
陈刚,周杰,张雪君,等．基于BP 与RBF 级联神经网络的日负荷预测[J]．电网技术,2009,33(12)：101-105．Chen Gang,Zhou Jie,Zhang Xuejun,et al．A daily load forecasting methods based on cascaded back propagation and radial basis function neural networks[J]．Power System Technology,2009,33(12)：101-105(in Chinese)．
[7]
张平,潘学萍,薛文超．基于小波分解模糊灰色聚类和BP神经网络的短期负荷预测[J]．电力自动化设备,2012,32(11)：121-125,141．Zhang Ping,Pan Xueming,Xue Wenchao．Short-term load forecasting based on wavelet decomposition, fuzzy gray correlation clustering and BP neural network[J]．Electric Power Automation Equipment ,2012,32(11)：121-1125,141(in Chinese)．
[8]
杨奎河,王宝树,赵玲玲．基于神经网络矫正的非线性短时负荷预测模型[J]．系统工程与电子技术,2004,26(11)：1710-1713．Yang Kuihe,Wang Baoshu,Zhao Lingling．Nonlinear short-term load forecasting model based on neural networks correction[J]．Systems Engineering and Electronics,2004,26(11)：1710-1713(in Chinese)．
[9]
周建中,张亚超,李清清,等．基于动态自适应径向基函数网络的概率性短期负荷预测[J]．电网技术,2010,34(3)：37-41．Zhou Jianzhong,Zhang Yachao,Li Qingqing,et al．Probabilistic short-term load forecasting based on dynamic self-adaptive radial basis function network[J]．Power System Technology,2010,34(3)：37-41(in Chinese)．
[10]
王孔森,盛戈皞,孙旭日,等．基于径向基神经网络的输电线路动态容量在线预测[J]．电网技术,2013,37(6)：1719-1725．Wang Kongsen,Sheng Gehao,Sun Xuri,et al．Online prediction of transmission dynamic line rating based on radial basis function neural network[J]．Power System Technology,2013,37(6)：1719-1725(in Chinese)．
[11]
刘瑞叶,黄磊．基于动态神经网络的风电场输出功率预测[J]．电力系统自动化,2012,36(11)：19-22,37．Liu Ruiye,WangLei．Wind power forecasting based on dynamic neural network[J]．Automation of Electric Systems,2012,36(11)：19-22,37(in Chinese)．
[12]
Du Juan．An improved TSK-Type dynamic fuzzy neural network approach for short-term load forecasting[J]．Power System Technology,2010,34(4)：69-75．
[13]
Jaeger H,Haas H．Harnessing nonlinearity：predicting chaotic systems and saving energy in wireless communication[J]．Science,2004,304(5667)：78-80．
[14]
Song Q S,Zhao X M,Feng Z,et al．Hourly electric load forecasting algorithm based on echo state neural network[C]//2011 23rd Chinese Control and Decision Conference．Mianyang,China：IEEE,2011：3893-3897．
[15]
嵇灵,牛东晓,吴焕苗．基于贝叶斯框架和回声状态网络的日最大负荷预测研究[J]．电网技术,2012,36(11)：82-86．Ji Ling,Niu Dongxiao,Wu Huanmiao．Daily peak load forecasting based on Bayesian framework and echo state network[J]．Power System Technology,2012,36(11)：82-86(in Chinese)．
[16]
Deihimi A,Showkati H．Application of echo state networks in short-term electric load forecasting[J]．Energy,2012,39(1)：327-340．
[17]
Deihimi A,Orang O,Showkati H．Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction[J]．Energy,2013,57(1)：382-401．
[18]
Shi Z,Han M．Support vector echo-state machine for chaotic time-series prediction[J]．IEEE Trans on Neural Networks,2007,18(2)：359-372．
[19]
Ma Q L,Chen W B．Modular state space of echo state network[J]．Neurocomputing,2013,122(25)：406-417．
[20]
Takens F．Detecting strange attractors in turbulence[J]．Lecture Notes in Math,1981,898(1)：366-381．
[21]
韩敏,史志伟,郭伟．储备池状态空间重构与混沌时间序列预测[J]．物理学报,2007,56(1)：43-50．Han Min,Shi Zhiwei,Guo Wei．Reservoir neural state reconstruction and chaotic time series prediction[J]．Acta Physica Sinica,2007,56(1)：43-50(in Chinese)
[22]
Sollich P,Krogh A．Learning with ensembles：how over-fitting can be useful[C]//1996 Advances in Neural Information Processing Systems．Cambridge,MA：MIT Press,1996：190-196．
