[1] 龚莺飞, 鲁宗相, 乔颖, 等. 光伏功率预测技术[J]. 电力系统自动化, 2016, 40(4): 140-151. GONG Y F, LU Z X, QIAO Y, et al.An overview of photovoltaic energy system output forecasting technology[J]. Automation of electric power systems, 2016, 40(4): 140-151. [2] DEV S, LEE Y, WINKLER S, et al.Color-based segmentation of sky/cloud images from ground-based cameras[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2016, 10(1): 231-242. [3] SUN X P, ZHANG T.Solar power prediction in smart grid based on NWP data and an improved boosting method[C]//IEEE International Conference on Energy Internet, Beijing, China, 2017. [4] 王丽婕, 王勃, 王铮, 等. 基于数学形态学聚类与果蝇优化算法的风电功率短期预测[J]. 太阳能学报, 2019, 40(12): 3621-3627. WANG L J, WANG B, WANG Z, et al.Wind power short-term prediction based on mathematical morphology cluster analysis and fruit fly optimization[J]. Acta energiae solaris sinica, 2019, 40(12): 3621-3627. [5] 丁明, 刘志, 毕锐, 等. 基于灰色系统校正-小波神经网络的光伏功率预测[J]. 电网技术, 2015, 39(9): 2438-2443. DING M, LIU Z, BI R, et al.Photovoltaic output prediction based on grey system correction-wavelet neural network[J]. Power system technology, 2015, 39(9): 2438-2443. [6] 张展耀. 基于深度学习理论的光伏功率短期预测研究[D]. 北京: 华北电力大学, 2019. ZHANG Z Y.The short-term solar power forecasting research based on deep learning theory[D]. Beijing: North China Electric Power University, 2019. [7] 叶林, 陈政, 赵永宁, 等. 基于遗传算法-模糊径向基神经网络的光伏发电功率预测模型[J]. 电力系统自动化, 2015, 39(16): 16-22. YE L, CHEN Z, ZHAO Y N, et al.Photovoltaic power forecasting model based on genetic algorithm and fuzzy radial basis function neural network[J]. Automation of electric power systems, 2015, 39(16): 16-22. [8] 高相铭, 杨世凤, 潘三博. 基于EMD和ABC-SVM的光伏并网系统输出功率预测研究[J]. 电力系统保护与控制, 2015, 43(21): 86-92. GAO X M, YANG S F, PAN S B.A forecasting model for output power of grid-connected photovoltaic generation system based on EMD and ABC-SVM[J]. Power system protection and control, 2015, 43(21): 86-92. [9] 管霖, 赵琦, 周保荣, 等. 基于多尺度聚类分析的光伏功率特性建模及预测应用[J]. 电力系统自动化, 2018, 42(15): 24-30, 232-236. GUAN L, ZHAO Q, ZHOU B R, et al. Multi-scale clustering analysis based modeling of photovoltaic power characteristics and its application in prediction[J]. Automation of electric power systems, 2018, 42(15): 24-30, 232-236. [10] 丁明, 缪乐颖, 车建峰, 等. 基于波动过程匹配技术的短期风电功率预测[J]. 电网技术, 2018, 42(11): 3652-3659. DING M, MIAO L Y, CHE J F, et al.Short-term wind power forecasting based on fluctuation process matching technology[J]. Power system technology, 2018, 42(11): 3652-3659. [11] SILVA T, MONTEIRO R, MOURA F, et al.Performance analysis of neural network training algorithms and support vector machine for power generation forecast of photovoltaic panel[J]. IEEE Latin America transactions, 2017, 15(6): 1091-1100. [12] GAO Y J, ZHU J, CHENG H X, et al.Study of short-term photovoltaic power forecast based on error calibration under typical climate categories[J]. Energies, 2016, 9(7): 1-15. [13] LORENZ E, HURKA J, HEINEMANN D, et al.Irradiance forecasting for the power prediction of grid-connected photovoltaic systems[J]. IEEE journal of selected topics in applied earth observation and remote sensing, 2009, 2(1): 2-10. [14] 殷豪, 陈云龙, 孟安波, 等. 基于二次自适应支持向量机的光伏输出功率预测[J]. 太阳能学报, 2019, 40(7): 1866-1873. YIN H, CHEN Y L, MENG A B, et al.Forecasting photovoltaic power based on quadric self-adaptive SVM model[J]. Acta energiae solaris sinica, 2019, 40(7): 1866-1873. [15] PUGGINI L, MCLOONE S.An enhanced variable selection and isolation forest based methodology for anomaly detection with OES data[J]. Engineering applications of artificial intelligence, 2016, 67: 126-135. [16] 李梅, 宁德军, 郭佳程. 基于注意力机制的CNN-LSTM模型及其应用[J]. 计算机工程与应用, 2019, 55(13): 20-27. LI M, NING D J, GUO J C.Attention mechanism-based CNN-LSTM model and its application[J]. Computer engineering and application, 2019, 55(13):20-27. [17] 史坤鹏, 乔颖, 赵伟, 等. 计及历史数据熵关联信息挖掘的短期风电功率预测[J]. 电力系统自动化, 2017, 41(3): 13-18. SHI K P, QIAO Y, ZHAO W, et al.Short-term wind power prediction based on entropy association information mining of historical data[J]. Automation of electric power systems, 2017, 41(3): 13-18. [18] 吉锌格, 李慧, 刘思嘉, 等. 基于MIE-LSTM的短期光伏功率预测[J]. 电力系统保护与控制, 2020, 48(7): 50-57. JI X G, LI H, LIU S J, et al.Short-term photovoltaic power forecasting based on MIE-LSTM[J]. Power system protection and control, 2020, 48(7): 50-57. |