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Review of Photovoltaic Power Output Prediction Technology |
Lai Changwei, Li Jinghua, Chen Bo, Huang Yujin, Wei Shanyang |
Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology School of Electrical Engineering Guangxi University Nanning 530004 China |
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Abstract Accurate prediction of photovoltaic power output is of great significance to ensure the security, stability and economic operation of the system after high proportion of PV access. At present, the research of photovoltaic power output prediction technology is still at stage of extensive research in our country. The research results of photovoltaic power output prediction technology are summarized in this paper. Firstly, the development of photovoltaic power generation system and its forecasting status are analyzed. Then, the current prediction methods and technologies, the measurement index of prediction effect and so on are combed, classified, summarized and commented respectively from the three aspects of point prediction, interval prediction and probability prediction. Finally, the future research direction of photovoltaic development and output prediction are discussed according to the current situation and development trend of photovoltaic industry in China. It is hoped that this work can provide reference for researchers in the field of photovoltaic power generation prediction.
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Received: 13 March 2018
Published: 29 March 2019
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[1] 太阳能发展“十三五”规划[EB/OL]. 国家能源局, 2016-12-08. http://zfxxgk.nea.gov.cn/auto87/201612/ t20161216_2358.htm. [2] 贾科, 宣振文, 林瑶琦, 等. 基于Adaboost算法的并网光伏发电系统的孤岛检测法[J]. 电工技术学报, 2018, 33(5): 1106-1113. Jia Ke, Xuan Zhenwen, Lin Yaoyi, et al.An islanding detection method for grid-connected photovoltaic power system based on Adaboost algorithm[J]. Transactions of China Electrotechnical Society, 2018, 33(5): 1106-1113. [3] 2017年全球新增光伏装机容量102GW同比增长33.7%[EB/OL]. 索比光伏网, 2018-04-25. https:// news.solarbe.com/201804/25/286571.html. [4] 单英浩, 付青, 耿炫, 等. 基于改进BP-SVM-ELM与粒子化SOM-LSF的微电网光伏发电组合预测方法[J]. 中国电机工程学报, 2016, 36(12): 3334-3343. Shan Yinghao, Fu Qing, Geng Xuan, et al.Combined forecasting of photovoltaic power generation in microgrid based on the improved BP-SVM-ELM and SOM-LSF with particlization[J]. Proceedings of the CSEE, 2016, 36(12): 3334-3343. [5] 雷鸣宇, 杨子龙, 王一波, 等. 光/储混合系统中的储能控制技术研究[J]. 电工技术学报, 2016, 31(23): 86-92. Lei Mingyu, Yang Zilong, Wang Yibo, et al.Study on control technology of energy storage station in photovoltaic/storage system[J]. Transactions of China Electrotechnical Society, 2016, 31(23): 86-92. [6] 田春光, 田利, 李德鑫, 等. 基于混合储能系统跟踪光伏发电输出功率的控制策略[J]. 电工技术学报, 2016, 31(14): 75-83. Tian Chunguang, Tian Li, Li Dexin, et al.Control strategy for tracking the output power of photovoltaic power generation based on hybrid energy storage system[J]. Transactions of China Electrotechnical Society, 2016, 31(14): 75-83. [7] 熊威明, 朱桂萍, 张达, 等. 可再生能源经济决策支持工具IRPP的构建及分析案例[J]. 可再生能源, 2013, 31(4): 65-70. Xiong Weiming, Zhu Guiping, Zhang Da, et al.Economic decision support tool of renewable energy for wind and PV power projects: IRPP model and its application[J]. Renewable Energy Resources, 2013, 31(4): 65-70. [8] 吉平, 周孝信, 宋云亭, 等. 区域可再生能源规划模型述评与展望[J]. 电网技术, 2013, 37(8): 2071-2079. Ji Ping, Zhou Xiaoxin, Song Yunting, et al.Review and prospect of regional renewable energy planning models[J]. Power System Technology, 2013, 37(8): 2071-2079. [9] 王洪坤, 葛磊蛟, 李宏伟, 等. 分布式光伏发电的特性分析与预测方法综述[J]. 电力建设, 2017, 38(7): 1-9. Wang Hongkun, Ge Leijiao, Li Hongwei, et al.A review on characteristic analysis and prediction method of distributed PV[J]. Electric Power Con- struction, 2017, 38(7): 1-9. [10] 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 Yingfei, Lu Zongxiang, Qiao Ying, et al.An overview of photovoltaic energy system output forecasting technology[J]. Automation of Electric Power Systems, 2016, 40(4): 140-151. [11] 王守相, 张娜. 基于灰色神经网络组合模型的光伏短期出力预测[J]. 电力系统自动化, 2012, 36(19): 37-41. Wang Shouxiang, Zhang Na.Short-term output power forcast of photovoltaic based on a grey and neural network hybrid model[J]. Automation of Electric Power Systems, 2012, 36(19): 37-41. [12] Pelland S, Remund J, Kleissl J, et al.Photovoltaic and solar forecasting: state of the art[R]. IEA PVPS Task, 2013. [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 Observations and Remote Sensing, 2009, 2(1): 2-10. [14] 陈志宝, 丁杰, 周海, 等. 地基云图结合径向基函数人工神经网络的光伏功率超短期预测模型[J]. 中国电机工程学报, 2015, 35(3): 561-567. Chen Zhibao, Ding Jie, Zhou Hai, et al.A model of very short-term photovoltaic power forecasting based on ground-based cloud images and RBF neural network[J]. Proceedings of the CSEE, 2015, 35(3): 561-567. [15] 赵唯嘉, 张宁, 康重庆. 光伏发电出力的条件预测误差概率分布估计方法[J]. 电力系统自动化, 2015, 39(16): 8-15. Zhao Weijia, Zhang Ning, Kang Chongqing, et al.A method of probabilistic distribution estimation of conditional forecast error for photovoltaic power generation[J]. Automation of Electric Power Systems, 2015, 39(16): 8-15. [16] 荆博. 光伏电站短期功率预测方法研究[D]. 镇江:江苏大学, 2017. [17] Ma Tao, Yang Hongxing, Lu Lin.Solar photovoltaic system modeling and performance prediction[J]. Renewable and Sustainable Energy Reviews, 2014, 36(36): 304-315. [18] Almonacid F, Prez-Higueras P J, Fernandez E F, et al. A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator[J]. Energy Conversion and Management, 2014, 85(9): 389-398. [19] 叶林, 赵永宁. 基于空间相关性的风电功率预测研究综述[J]. 电力系统自动化, 2014, 38(14): 126-135. Ye Lin, Zhao Yongning.A review on wind power prediction based on spatial correlation approach[J]. Automation of Electric Power System, 2014, 38(14): 126-135. [20] Alam S.Prediction of direct and global solar irradiance using broadband models: validation of REST model[J]. Renewable Energy, 2006, 31(8): 1253-1263. [21] 赵书强, 王明雨, 胡永强, 等. 基于不确定理论的光伏出力预测研究[J]. 电工技术学报, 2015, 30(16): 213-220. Zhao Shuqiang, Wang Mingyu, Hu Yongqiang, et al.Research on the prediction of PV output based on uncertainty theory[J]. Transactions of China Electro- technical Society, 2015, 30(16): 213-220. [22] Li Yanting, Su Yan, Shu Lianjie.An ARMAX model for forecasting the power output of a grid connected photovoltaic system[J]. Renewable Energy, 2014, 66(6): 78-89. [23] 黄磊, 舒杰, 姜桂秀, 等. 基于多维时间序列局部支持向量回归的微网光伏发电预测[J]. 电力系统自动化, 2014, 38(5): 19-24. Huang Lei, Shu Jie, Jiang Guixiu, et al.Photovoltaic generation forecast based on multid mensional time-seris and local support vector regression in microgrid[J]. Automation of Electric Power Systems, 2014, 38(5): 19-24. [24] Persson C, Bacher P, Shiga T, et al.Multi-site solar power forecasting using gradient boosted regression trees[J]. Solar Energy, 2017, 150: 423-436. [25] 蔡金锭, 叶荣, 陈汉城. 回复电压多元参数回归分析的油纸绝缘老化诊断方法[J]. 电工技术学报, 2018, 33(21): 5080-5089. Cai Jinding, Ye Rong, Chen Hancheng.Aging diagnosis method of oil-paper insulation based on multiple parameter regression analysis of recovery voltage[J]. Transactions of China Electrotechnical Society, 2018, 33(21): 5080-5089. [26] Li Yingzi, Wang Zefeng, Niu Jincang.Forecast of power generation for grid-connected photovoltaic system based on grey theory and verification model[C]//IEEE Fourth International Conference on Intelligent Control and Information Processing, Beijing, China, 2013: 129-133. [27] 侯伟, 肖健, 牛利勇. 基于灰色理论的光伏发电系统出力预测方法[J]. 电气技术, 2016, 17(4): 53-58. Hou Wei, Xiao Jian, Niu Liyong.Analysis of power generation capacity of photovoltaic power[J]. Electrical Engineering, 2016, 17(4): 53-58. [28] Yazdanbaksh O, Krahn A, Dick S.Predicting solar power output using complex fuzzy logic[C]//Joint IFSA World Congress and NAFIPS Annual Meeting Edmonton: IFSA World Congress and NAFIPS Annual Meeting, Edmonton, AB, Canada, 2013: 1243-1248. [29] Yang C, Thatte A A, Xie L.Multitime-scale data- driven spatio-temporal forecast of photovoltaic generation[J]. IEEE Transactions on Sustainable Energy, 2015, 6(1): 104-112. [30] 叶瑞丽, 郭志忠, 刘瑞叶, 等. 基于小波包分解和改进Elman神经网络的风电场风速和风电功率预测[J]. 电工技术学报, 2017, 32(21): 103-111. Ye Ruili, Guo Zhizhong, Liu Ruiye, et al.Wind speed and wind power forecasting method based on wavelet packet decomposition and improved Elman neural network[J]. Transactions of China Electrotechnical Society, 2017, 32(21): 103-111. [31] David M, Ramahatana F, Trombe P J, et al.Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models[J]. Solar Energy, 2016, 133: 55-72. [32] 李鹏梅, 臧传治, 王侃侃. 基于相似日和神经网络的光伏发电预测[J]. 可再生能源, 2013, 31(10): 1-4. Li Pengmei, Zang Chuanzhi, Wang Kankan.Photovoltaic generation prediction based on similar days and neural network[J]. Renewable Energy Resources, 2013, 31(10): 1-4. [33] Gao Yajing, Zhu Jing, Cheng Huaxin, et al.Study of short-term photovoltaic power forecast based on error calibration under typical climate categories[J]. Energies, 2016, 9(7): 1-15. [34] 罗建春, 晁勤, 罗洪, 等. 基于LVQ-GA-BP神经网络光伏电站出力短期预测[J]. 电力系统保护与控制, 2014, 42(13): 89-94. Luo Jianchun, Chao Qin, Luo Hong, et al.PV short- term output forecasting based on LVQ-GA-BP neural network[J]. Power System Protection and Control, 2014, 42(13): 89-94. [35] Huang Long, Zhang Zijun, Yan Su.Analysis of daily solar power prediction with data-driven approaches[J]. Applied Energy, 2014, 126: 29-37. [36] 朱永强, 田军. 最小二乘支持向量机在光伏功率预测中的应用[J]. 电网技术, 2011, 35(7): 54-59. Zhu Yongqiang, Tian Jun.Application of least square support vector machine in photovoltaic power forecasting[J]. Power System Technology, 2011, 35(7): 54-59. [37] Liu Huichao, Tian Hongqi, Li Yanfei.Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction[J]. Applied Energy, 2012, 98(1): 415-424. [38] Soubdhan T, Ndong J, Ould-Baba H, et al.A robust forecasting framework based on the Kalman filtering approach with a twofold parameter tuning procedure: Application to solar and photovoltaic prediction[J]. Solar Energy, 2016, 131: 246-259. [39] 丁明, 徐宁舟. 基于马尔可夫链的光伏发电系统输出功率短期预测方法[J]. 电网技术, 2011, 39(1): 152-157. Ding Ming, Xu Ningzhou.A method to forecast short-term output power of photovoltaic generation system based on Markov chain[J]. Power System Technology, 2011, 39(1): 152-157. [40] Eseye A T, Zhang J, Zheng D.Short-term photo- voltaic solar power forecasting using a hybrid wavelet-PSO-SVM model based on SCADA and meteorological information[J]. Renewable Energy, 2017, 118: 357-367. [41] 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 Lin, Chen Zheng, Zhao Yongning, 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. [42] Marquez R, Coimbra C F M. Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database[J]. Solar Energy, 2011, 85(5): 746-756. [43] Li Zhiyong, Zhou Yunlei, Cheng Cheng, et al.Short term photovoltaic power generation forecasting using RBF neural network[C]//The 26th Chinese Control and Decision Conference, Changsha, 2014: 2758-2763. [44] Liu Luyao, Liu Diran, Sun Qie, et al.Forecasting power output of photovoltaic system using a BP network method[J]. Energy Procedia, 2017, 142: 780-786. [45] 张艳霞, 赵杰. 基于反馈型神经网络的光伏系统发电功率预测[J]. 电力系统保护与控制, 2011, 39(15): 96-101. Zhang Yanxia, Zhao Jie.Application of recurrent neural networks to generated power forecasting for photovoltaic system[J]. Power System Protection and Control, 2011, 39(15): 96-101. [46] Yang H T, Huang C M, Huang Y C, et al.A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output[J]. IEEE Transa- ctions on Sustainable Energy, 2014, 5(3): 917-926. [47] 王新普, 周想凌, 邢杰, 等. 一种基于改进灰色BP神经网络组合的光伏出力预测方法[J]. 电力系统保护与控制, 2016, 44(18): 81-87. Wang Xinpu, Zhou Xiangling, Xing Jie, et al.A prediction method of PV output power based on the combination of improved grey back propagation neural network[J]. Power System Protection and Control, 2016, 44(18): 81-87. [48] Chow S K H, Lee E W M, Li D H W. Short-term prediction of photovoltaic energy generation by intelligent approach[J]. Energy and Buildings, 2012, 55: 660-667. [49] 陈昌松, 段善旭, 蔡涛, 等. 基于模糊识别的光伏发电短期预测系统[J]. 电工技术学报, 2011, 26(7): 83-89. Chen Changsong, Duan Shanxu, Cai Tao, et al.Short- term photovoltaic generation forecasting system based on fuzzy recognition[J]. Transactions of China Electrotechnical Society, 2011, 26(7): 83-89. [50] Liao C C.Genetic K-means algorithm based RBF network for photovoltaic MPP prediction[J]. Energy, 2010, 35(2): 529-536. [51] Bae K Y, Han S J, Dan K S.Hourly solar irradiance prediction based on support vector machine and its error analysis[J]. IEEE Transactions on Power Systems, 2017, 32(2): 935-945. [52] 姚仲敏, 潘飞, 沈玉会, 等. 基于GA-BP和POS-BP神经网络的光伏电站出力短期预测[J]. 电力系统保护与控制, 2015, 43(20): 83-89. Yao Zhongmin, Pan Fei, Shen Yuhui, et al.Short- term prediction of photovoltaic power generation output[J]. Power System Protection and Control, 2015, 43(20): 83-89. [53] Malvoni M, Degiorgi M G, Congedo P M.Photo- voltaic forecast based on hybrid PCA-LSSVM using dimensionality reducted data[J]. Neurocomputing, 2016, 211: 72-83. [54] 严毅, 张承慧, 李珂, 等. 含压缩空气的微网复合储能系统主动控制策略[J]. 电工技术学报, 2017, 32(20): 231-240. Yan Yi, Zhang Chenghui, Li Ke, et al.An active control strategy for composited energy storage with compressed air energy storage in micro-grid[J]. Transactions of China Electrotechnical Society, 2017, 32(20): 231-240. [55] Bouzerdoum M, Mellit A, Pavan A M.A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant[J]. Solar Energy, 2013, 98(4): 226-235. [56] 张学清, 梁军, 张熙, 等. 基于样本熵和极端学习机的超短期风电功率组合预测模型[J]. 中国电机工程学报, 2013, 33(25): 33-40. Zhang Xueqing, Liang Jun, Zhang Xi, et al.Combined model for ultra short-term wind power prediction based on sample entropy and extreme learning machine[J]. Proceedings of the CSEE, 2013, 33(25): 33-40. [57] 茆美琴, 龚文剑, 张榴晨, 等. 基于EEMD-SVM方法的光伏电站短期出力预测[J]. 中国电机工程学报, 2013, 33(34): 17-24. Mao Meiqin, Gong Wenjian, Zhang Liuchen, et al.Short-term photovoltaic generation forecasting based on EEMD-SVM combined method[J]. Proceedings of the CSEE, 2013, 33(34): 17-24. [58] Huang J, Korolkiewicz M, Agrawal M, et al.Forecasting solar radiation on an hourly time scale using a coupled auto regressive and dynamical system (CARDS) model[J]. Solar Energy, 2013, 87(1): 136-149. [59] 耿建军, 吕卫民, 董景荣. 基于GRNN神经网络的变权组合预测的权重确定方法[J]. 数学的实践与认识, 2011, 41(3): 86-93. Geng Jianjun, Lü Weimin, Dong Jingrong.A new mothod for the estimation of optimum weight coefficients of the weight changeable combination forecasts bases on the generalized regression neural network[J]. Mathematics in Practice and Theory, 2011, 41(3): 86-93. [60] 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 prediction for wind power based on conditional Copula function[J]. Proceedings of the CSEE, 2016, 36(S1): 79-86. [61] Wan Can, Lin Jin, Song Yonghua, et al.Probabilistic forecasting of photovoltaic generation: an efficient statistical approach[J]. IEEE Transactions on Power Systems, 2017, 32(3): 2471-2472. [62] Ranna M, Koprinska I, Agelidis V G.2D-interval forecasts for solar power production[J]. Solar Energy, 2015, 122: 191-203. [63] Chai Songjian, Xu Zhao, Wong W K.Optimal granule-based PIs construction for solar irradiance forecast[J]. IEEE Transactions on Power Systems, 2016, 31(4): 3332-3333. [64] Wang Suqin, Jia Cuiling.Prediction Intervals for short-term photovoltaic generation forecasts[C]//Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control, Qinhuangdao, China, 2015: 459-463. [65] Chai Songjian, Niu Ming, Xu Zhao, et al.Nonpara- metric conditional interval forecasts for PV power generation considering the temporal dependence[C]// Power and Energy Society General Meeting, Boston, MA, USA, 2016: 1-5. [66] Trapero J R.Calculation of solar irradiation prediction intervals combining volatility and kernel density estimates[J]. Energy, 2016, 114: 266-274. [67] Torregrossa D, Boudec J-Y L, Paolone M. Model-free computation of ultra-short-term prediction intervals of solar irradiance[J]. Solar Energy, 2016, 124: 57-67. [68] 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. [69] 康重庆, 夏清, 刘梅. 电力系统负荷预测[M]. 北京: 中国电力出版社, 2017. [70] Zamo M, Mestre O, Arbogast P, et al.A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production: Part Ⅱ probabilistic forecast of daily production[J]. Solar Energy, 2014, 105: 804-816. [71] 董雷, 周文萍, 张沛, 等. 基于动态贝叶斯网络的光伏发电短期概率预测[J]. 中国电机工程学报, 2013, 33(增刊1): 38-45. Dong Lei, Zhou Wenping, Zhang Pei, et al.Short- term photovoltaic output forecast based on dynamic bayesian network theory[J]. Proceedings of the CSEE, 2013, 33(S1): 38-45. [72] 何耀耀, 许启发, 杨善林. 基于RBF神经网络分位数回归的电力负荷概率密度预测方法[J]. 中国电机工程学报, 2013, 33(1): 93-98. He Yaoyao, Xu Qifa, Yang Shanlin.A power load probability density forecasting method based on RBF neural network quantile regression[J]. Proceedings of the CSEE, 2013, 33(1): 93-98. [73] Golestaneh F, Pinson P, Gooi H B.Very short-term nonparametric probabilistic forecasting of renewable energy generation-with application to solar energy[J]. IEEE Transactions on Power Systems, 2016, 31(5): 3850-3863. [74] Zhang Yao, Wang Jianxue.GEFCom2014 pro- babilistic solar power forecasting based on K-nearest neighbor and kernel density estimator[C]//Power & Energy Society General Meeting, Denver, CO, USA, 2015: 26-30. [75] 张昭, 王世山, 赵亮, 等. 多导体线束内串扰概率分布的预测[J]. 电工技术学报, 2017, 32(7): 204-214. Zhang Zhao, Wang Shishan, Zhao Liang, et al.Prediction of crosstalk probability distribution in cable bundles[J]. Transactions of China Electro- technical Society, 2017, 32(7): 204-214. [76] 周封, 金丽斯, 刘健, 等. 基于多状态空间混合Markov链的风电功率概率预测[J]. 电力系统自动化, 2012, 36(6): 29-33. Zhou Feng, Jin Lisi, Liu Jian, et al.Probabilistic wind power forecasting based on multi-state space and hybrid Markov chain models[J]. Automation of Electric Power Systems, 2012, 36(6): 29-33. [77] Sanjari M J, Gooi H B.Probabilistic forecast of PV power generation based on higher-order Markov chain[J]. IEEE Power & Energy Society, 2016, 32(4): 2942-2952. [78] 袁晓玲, 施俊华, 徐杰彦. 计及天气类型指数的光伏发电短期出力预测[J]. 中国电机工程学报, 2013, 33(34): 57-64. Yuan Xiaoling, Shi Junhua, Xu Jieyan.Short-term power forecasting for photovoltaic generation considering weather type index[J]. Proceedings of the CSEE, 2013, 33(34): 57-64. [79] 李元诚, 王蓓, 王旭峰. 基于和声搜索-高斯过程混合算法的光伏发电出力预测[J]. 电力自动化设备, 2014, 34(8): 13-18. Li Yuancheng, Wang Bei, Wang Xufeng.Photo- voltaic power forecasting based on harmony search and gaussian process algorithms[J]. Electric Power Automation Equipment, 2014, 34(8): 13-18. [80] 李乐, 刘天琪. 基于近邻传播聚类和回声状态网络的光伏预测[J]. 电力自动化设备, 2016, 36(7): 41-46. Li Le, Liu Tianqi.PV power forecasting based on AP-ESN[J]. Electric Power Automation Equipment, 2016, 36(7): 41-46. [81] 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 Ming, Liu Zhi, Bi Rui, et al.Photovoltaic output prediction based on grey system correction- wavelet neural network[J]. Power System Technology, 2015, 39(9): 2438-2443. [82] 代倩, 段善旭, 蔡涛, 等. 基于天气类型聚类识别的光伏系统短期无辐照度发电预测模型研究[J]. 中国电机工程学报, 2011, 31(34): 28-35. Dai Qian, Duan Shanxu, Cai Tao, et al.Short-term PV generation system forecasting model without irradiation based on weather type clustering[J]. Proceedings of the CSEE, 2011, 31(34): 28-35. [83] 王飞, 米增强, 甄钊, 等. 基于天气状态模式识别的光伏电站发电功率分类预测方法[J]. 中国电机工程学报, 2013, 33(34): 75-82. Wang Fei, Mi Zengqiang, Zhen Zhao, et al.A classified forecasting approach of power generation for photovoltaic plants based on weather condition pattern recognition[J]. Proceedings of the CSEE, 2013, 33(34): 75-82. [84] 王晓兰, 葛鹏江. 基于相似日和径向基函数神经网络的光伏阵列输出功率预测[J]. 电力自动化设备, 2013, 33(1): 100-103. Wang Xiaolan, Ge Pengjiang.PV array output power forecasting based on similar day and RBFNN[J]. Electric Power Automation Equipment, 2013, 33(1): 100-103. [85] 傅美平, 马红伟, 毛建容. 基于相似日和最小二乘支持向量机的光伏发电短期预测[J]. 电力系统保护与控制, 2012, 40(16): 65-69. Fu Meiping, Ma Hongwei, Mao Jianrong.Short-term photovoltaic power forecasting based on similar days and least square support vector machine[J]. Power System Protection and Control, 2012, 40(16): 65-69. [86] 高阳, 张碧玲, 毛京丽, 等. 基于机器学习的自适应光伏超短期出力预测模型[J]. 电网技术, 2015, 39(2): 307-311. Gao Yang, Zhang Biling, Mao Jingli, et al.Machine learning-based adaptive very-short-term forecast model for photovoltaic power[J]. Power System Technology, 2015, 39(2): 307-311. [87] 陈道君, 龚庆武, 金朝意, 等. 基于自适应扰动量子粒子群算法参数优化的支持向量回归机短期风电功率预测[J]. 电网技术, 2013, 37(4): 974-980. Chen Daojun, Gong Qingwu, Jin Zhaoyi, et al.Short- term wind power prediction based on support vector regression machine optimized by adaptive disturbance quantum-behaved particle swarm optimization[J]. Power System Technology, 2013, 37(4): 974-980. [88] 朱想, 居蓉蓉, 程序, 等. 组合数值天气预报与地基云图的光伏超短期功率预测模型[J]. 电力系统自动化, 2015, 39(6): 4-10. Zhu Xiang, Ju Rongrong, Cheng Xu, et al.A very short-term prediction model for photovoltaic power based on numerical weather prediction and groond- based cloud images[J]. Automation of Electric Power Systems, 2015, 39(6): 4-10. [89] 陈昊, 万秋兰, 王玉荣. 基于厚尾均值广义自回归条件异方差族模型的短期风电功率预测[J]. 电工技术学报, 2016, 31(5): 91-98. Chen Hao, Wan Qiulan, Wang Yurong.Short-term wind power forecast based on fat-tailed generalized autoregressive conditional heteroscedasticity-in-mean type models[J]. Transactions of China Electro- technical Society, 2016, 31(5): 91-98. [90] 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 intervals forecasts of wind power based on PSO-KELM[J]. Proceedings of the CSEE, 2015, 35(S1): 146-153. [91] 章国勇, 伍永刚, 张洋, 等. 一种风电功率混沌时间序列概率区间简易预测模型[J]. 物理学报, 2014, 63(13): 430-438. Zhang Guoyong, Wu Yonggang, Zhang Yang, et al.A simple model for probabilistic interval forecasts of wind power chaotic time series[J]. Acta Physica Sinicam, 2014, 63(13): 430-438. [92] Quan Hao, Srinivasan D, Khosravi A.Short-term load and wind power forecasting using neural network-based prediction intervals[J]. IEEE Transa- ctions on Neural Networks and Learning Systems, 2014, 25(2): 303-315. [93] Ni Qiang, Zhuang Shengxian, Sheng Hanming, et al.An ensemble prediction intervals approach for short-term PV power forecasting[J]. Solar Energy, 2017, 155: 1072-1083. [94] 于鹏, 黎静华, 文劲宇, 等. 含风电功率时域特性的风电功率序列建模方法[J]. 中国电机工程学报, 2014, 34(22): 3715-3723. Yu Peng, Li Jinghua, Wen Jinyu, et al.A wind power time series modeling method based on its time domain characteristics[J]. Proceedings of the CSEE, 2014, 34(22): 3715-3723. [95] 2017年前三季度新增光伏装机42GW![EB/OL]. 中国新能源网, 2017-10-19. http://www.china-nengyuan. com/news/115613.html. [96] 从国家规划看分布式光伏发展前景[EB/OL]. 北极星太阳能光伏网, 2017-08-23. http://guangfu.bjx. com.cn/news/20170823/845230.html. |
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