[1] 谷兴凯, 范高锋, 王晓蓉, 等. 风电功率预测技术综述[J]. 电网技术, 2007, 31(S2): 335-338. GU X K, FAN G F, WANG X R, et al.Summarization of wind power prediction technology[J]. Power system technology, 2007, 31(S2): 335-338. [2] PONCELA M, PONCELA P, PERÁN J R. Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting[J]. Applied energy, 2013, 108: 349-362. [3] LAHOUAR A, BEN H S J. Hour-aheadwind power forecast based on random forests[J]. Renewable energy,2017, 109: 529-541. [4] 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 Q M,CHEN L, CHENG Y M, 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. [5] LIU G, YU W D.The BP neural network modeling on worsted spinning with grey Su perior theory and correlation analysis[C]//Fourth International Conference on Natural Computation, Ji’nan, China, 2008: 374-378. [6] 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(S1): 146-153. YANG X Y, GUAN W Y, LIU Y Q, et al.Prediction intervals forecasts of wind power based on PSO-KELM[J]. Proceedings of the CSEE, 2015, 35(S1): 146-153. [7] 朱抗, 杨洪明, 孟科. 基于极限学习机的短期风力发电预测[J]. 电力科学与技术学报, 2019, 34(2): 106-111. ZHU K, YANG H M, MENG K.Short-term wind power forecast using extreme learning machine[J]. Journal of electric power science and technology, 2019, 34(2): 106-111. [8] WAN C, XU Z.Probabilistic forecasting of wind power generation using extreme learning machine[J]. IEEE transactions on power systems, 2014, 29(3): 1-11. [9] 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 Z, PEI Y, CAO L X, et al.Review of wind power forecasting method[J]. High voltage engineering, 2016, 42(4): 1047-1060. [10] 姜贵敏, 陈志军, 李笑竹, 等. 基于EEMD-ACS-LSSVM的短期风电功率预测[J]. 太阳能学报, 2020, 41(5): 77-84. JIANG G M, CHEN Z J, LI X Z, et al.Short-term prediction of wind power based on EEMD-ACS-LSSVM[J]. Acta energiae solaris sinica, 2020, 41(5): 77-84. [11] 彭晨宇, 陈宁, 高丙团. 结合多重聚类和分层聚类的超短期风电功率预测方法[J]. 电力系统自动化, 2020, 44(2): 173-180. PENG C Y, CHEN N, GAO B T.Ultra-short-term wind power forecasting method combining multiple clustering and hierarchical clustering[J]. Automation of electric power systems, 2020, 44(2): 173-180. [12] 赵征, 汪向硕. 基于CEEMD和改进时间序列模型的超短期风功率多步预测[J]. 太阳能学报, 2020, 41(7):352-358. ZHAO Z, WANG X S.Ultra-short-term multi-step wind power prediction based on CEEMD and improved time series model[J]. Acta energiae solaris sinica, 2020, 41(7): 352-358. [13] 武小梅, 林翔, 谢旭泉, 等. 基于VMD-PE和优化相关向量机的短期风电功率预测[J]. 太阳能学报, 2018, 39(11): 3277-3285. WU X M, LIN X, XIE X Q, et al.Short-term wind power forecasting based on variational mode decomposition-permutation entropy and optimized relevance vector machine[J]. Acta energiae solaris sinica, 2018, 39(11):3277-3285. [14] DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J]. IEEE transactions on signal processing, 2013, 62(3): 531-544. [15] OTTEN A.Note on the Spearman rank correlation coefficient[J]. Journal of the American Statistical Association, 1973, 68(343): 585. [16] YI Y, SHANG P J.Weighted permutation entropy based on different symbolic approaches for financial time series[J]. Physica A: statistical mechanics and its applications, 2016, 443: 137-148. [17] 薛建凯. 一种新型的群智能优化技术的研究与应用[D]. 上海: 东华大学, 2020. XUE J K.Research and application of a novel swarm intelligence optimization technique: sparrow search algorithm[D]. Shanghai: Donghua University, 2020. [18] 刘栋, 魏霞, 王维庆, 等. 基于SSA-ELM的短期风电功率预测[J]. 智慧电力, 2021, 49(6): 53-59, 123. LIU D, WEI X, WANG W Q, et al.Short-term wind power prediction based on SSA-ELM[J]. Smart power, 2021, 49(6): 53-59, 123. [19] HUANG G B,WANG D H, LAN Y.Extreme learning machines: a survey[J]. International journal of machine learning and cybernetics, 2011, 2(2): 107-122. |