Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (5): 70-76.doi: 10.6040/j.issn.1672-3961.0.2019.706

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Interval prediction of short-term regional photovoltaic power based on BP neural network

SUN Donglei1, WANG Yan1, YU Yixiao2*, HAN Xueshan2, YANG Ming2, YAN Fangqing2   

  1. 1. Economic and Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, Shandong, China;
    2. School of Electrical Engineering, Shandong University, Jinan 250061, Shandong, China
  • Published:2020-10-19

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Abstract: High penetration photovoltaic power brought severe challenges to the sage and stable operation of power systems. Considering the limitations of individual photovoltaic power prediction, this paper proposed an interval prediction method of regional photovoltaic power based on BP neural network, which extracted correlation through mutual information, and applied principal component analysis(PCA)to data dimensionality reduction. Taking the advantages of the data mining and nonlinear relation fitting for BP neural network, the uncertainty of regional photovoltaic power prediction could be quantified by the combining the neural networks and nonparametric probabilistic prediction methods. Experimental example used 10 photovoltaic plants in an area in China and compared the method of no data dimension reduction of original data with the proposed method in this paper. The 80% and 90% confidence intervals of the two models were calculated, which showed that the confidence intervals of the proposed mothed were narrower. At the same time, the proposed model predicted the 80% and 90% confidence intervals with 72 hours in advance, the results verified the feasibility and advance of the proposed method.

Key words: regional photovoltaic power, prediction intervals, BP neural network, dimensionality reduction, principal component analysis

CLC Number: 

  • TM60
[1] LIN You, YANG Ming, WAN Can, et al. A multi-model combination approach for probabilistic wind power forecasting[J]. IEEE Transactions on Sustainable Energy, 2019, 10(1): 226-237.
[2] YANG Ming, FAN Shu, LEE W J. Probabilistic short-term wind power forecast using componential sparse bayesian learning[J]. IEEE Transactions on Industry Applications, 2013, 49(6):2783-2792.
[3] 赵争鸣,刘建政,孙晓英,等. 太阳能光伏发电其应用[M].北京:科学出版社, 2005.
[4] 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.
[5] RANA Mashud, IRENA Koprinska, AGELIDIS Vassilios G. Univariate and multivariate methods for very short-term solar photovoltaic power forecasting[J]. Energy Conversion and Management, 2016, 121:380-390.
[6] 赵唯嘉, 张宁, 康重庆, 等. 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(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(16):8-15.
[7] 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 multidimensional time-series and local support vector regression in microgrids[J]. Automation of Electric Power Systems, 2014, 38(5):19-24.
[8] 戴锦, 肖文波, 胡芳雨, 等. 光伏发电性能物理预测模型的研究[J]. 电源技术, 2018,42(2): 262-266. DAI Jin, XIAO Weibo, HU Fangyu, et al. Research of photovoltaic prediction model[J]. Power Supply Technology, 2018, 42(2): 262-266.
[9] 王育飞, 付玉超, 孙路, 等. 基于混沌-RBF神经网络的光伏发电功率超短期预测模型[J]. 电网技术, 2018,42(4):1110-1116. WANG Yufei, FU Yuchao, SUN Lu, et al. Ultra-short term prediction model of photovoltaic output power based on chaos-RBF neural network[J]. Power System Technology, 2018, 42(4):1110-1116.
[10] 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.
[11] ALHAKEEM D, MANDAL P, HAQUE A U, et al. A new strategy to quantify uncertainties of wavelet-GRNN-PSO based solar PV power forecasts using bootstrap confidence intervals[C] //Power and Energy Society General Meeting. Denver, USA: IEEE, 2015.
[12] TED Soubdhan, JOSEPH Ndong, HANANY Ould-Baba, 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.
[13] 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.
[14] 颜伟, 任洲洋, 赵霞, 等. 光伏电源输出功率的非参数核密度估计模型[J]. 电力系统自动化, 2013, 37(10):35-40. YAN Wei, REN Zhouyang, ZHAO Xia, et al. Probabilistic photovoltaic power modeling based on nonparametric kernel density estimation[J]. Automation of Electric Power Systems, 2013, 37(10):35-40.
[15] SCOLARI E, SOSSAN F, PAOLONE M. Irradiance prediction intervals for PV stochastic generation in microgrid applications[J]. Solar Energy, 2016, 139:116-129.
[16] 殷豪, 陈云龙, 孟安波, 等. 基于模糊信息粒化的光伏出力区间预测[J]. 电测与仪表, 2017, 55(14): 39-44. YIN Hao, CHEN Yunlong, MENG Anbo, et al. PV power interval prediction based on fuzzy information granulation[J]. Electrical Measurement & Instrumentation, 2017, 55(14): 39-44.
[17] 谭津, 邓长虹, 杨威, 等. 微电网光伏发电的Adaboost天气聚类超短期预测方法[J]. 电力系统自动化, 2017(21):39-45. TAN Jin, DENG Changhong, YANG Wei, et al. Ultra-short-term photovoltaic power forecasting in microgrid based on adaboost clustering[J]. Automation of Electric Power Systems, 2017(21):39-45.
[18] DOLARA A, LEVA S, MANZOLINI G. Comparison of different physical models for PV power output prediction[J]. Solar Energy, 2015, 119: 83-99.
[19] URQUHART B, KURTZ B, DAHLIN E, et al. Development of a sky imaging system for short-term solar power forecasting[J]. Atmospheric Measurement Techniques, 2015, 8(2):875-890.
[20] 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):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 CESS, 2013, 33(Suppl.):38-45.
[21] 朱雪龙. 应用信息论基础[M]. 北京:清华大学出版社, 2001.
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