[1] 徐政, 刘滨, 熊强, 等. 多地区太阳能资源的监测与分析[J]. 太阳能学报, 2020, 41(10): 174-181. XU Z, LIU B, XIONG Q, et al.Monitoring and analysis of solar energy resources in different areas[J]. Acta energiae solaris sinica, 2020, 41(10): 174-181. [2] NAM S, HUR J.A hybrid spatio-temporal forecasting of solar generating resources for grid integration[J]. Energy, 2019, 177: 503-510. [3] LI Q, WU Z, ZHANG H J.Spatio-temporal modeling with enhanced flexibility and robustness of solar irradiance prediction: a chain-structure echo state network approach[J]. Journal of cleaner production, 2020, 261: 121151. [4] 贾东于, 李开明, 杨丽薇, 等. CMIP5气候模式对未来30年太阳辐射变化的预估研究[J]. 太阳能学报, 2020, 41(3): 199-205. JIA D Y, LI K M, YANG L W, et al.Prediction of solar radiation variation in future by CMIP5 climate model[J]. Acta energiae solaris sinica, 2020, 41(3): 199-205. [5] TASCIKARAOGLU A, SANANDAJI B M, CHICCO G, et al.Compressive spatio-temporal forecasting of meteorological quantities and photovoltaic power[J]. IEEE transactions on sustainable energy, 2016, 7(3): 1295-1305. [6] LAN H, YIN Y, HONG Y Y, et al.Day-ahead spatio-temporal forecasting of solar irradiation along a navigation route[J]. Applied energy, 2018, 211: 15-27. [7] MOORE D S.Statistics: concepts and controversies[M]. New York: W. H. Freeman and Company, 2017: 290-296. [8] 邓威, 郭钇秀, 李勇, 等. 基于特征选择和Stacking集成学习的配电网网损预测[J]. 电力系统保护与控制, 2020, 48(15): 108-115. DENG W, GUO Y X, LI Y, et al.Power losses prediction based on feature selection and stacking integrated learning[J]. Power system protection and control, 2020, 48(15): 108-115. [9] HUANG G B, ZHU Q Y, SIEW C K.Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1): 489-501. [10] 王琦, 季顺祥, 钱子伟, 等. 基于熵理论和改进ELM的光伏发电功率预测[J]. 太阳能学报, 2020, 41(10): 151-158. WANG Q, JI S X, QIAN Z W, et al.Photovoltaic power prediction based on entropy theory and improved ELM[J]. Acta energiae solaris sinica, 2020, 41(10): 151-158. [11] 靳果, 朱清智, 闫奇. 基于PCA和ML-ELM-AE的短期光伏功率预测[J]. 控制工程, 2021(9): 1787-1796. JIN G, ZHU Q Z, YAN Q.Short-term output power prediction of photovoltaic system based on PCA and ML-ELM-AE[J]. Control engineering of China, 2021(9): 1787-1796. [12] 周子东, 李东伟, 李国胜, 等. 基于逐步回归的AdaBoost-SVR模型在海上风电项目造价预测中的应用[J]. 太阳能学报, 2020, 41(7): 259-264. ZHOU Z D, LI D W, LI G S, et al.Application of AdaBoost-SVR model based on stepwise regression in cost forecast of offshore wind power[J]. Acta energiae solaris sinica, 2020, 41(7): 259-264. [13] 李军, 闫佳佳. 基于KELM-AdaBoost方法的短期风电功率预测[J]. 控制工程, 2019, 26(3): 492-501. LI J, YAN J J.Short-term wind power forecasting based on KELM-AdaBoost method[J]. Control engineering of China, 2019, 26(3): 492-501. [14] 唐雅洁, 林达, 倪筹帷, 等. 基于XGBoost的双层协同实时校正超短期光伏预测[J]. 电力系统自动化, 2021, 45(7): 18-27. TANG Y J, LIN D, NI C W, et al.XGBoost based bi-layer collaborative real-time calibration for ultra-short-term photovoltaic prediction[J]. Automation of electric power systems, 2021, 45(7): 18-27. |