太阳能光伏发电量预测方法综述
Review of Solar Photovoltaic Power Generation Forecasting
DOI: 10.12677/JSTA.2021.91001, PDF,   
作者: 万 贝, 姚彦鑫*, 黄雅琦：北京信息科技大学光电测试技术及仪器教育部重点实验室位，北京；北京信息科技大学高端装备智能感知与控制北京市国际科技合作基地位，北京
关键词: 太阳能；光伏预测；神经网络；点预测法；概率预测法； Solar Energy； Photovoltaic Prediction； Neural Network； Point Prediction Method； Probability Prediction Method
摘要: 本文主要讲述当前能源储备状况及未来预期的情况，分析太阳能的特性及使用太阳能光伏发电的意义和价值，并对此前相关研究进行总结。对当前国内外的主流的太阳能光伏预测方法进行了详尽的分类，分析了各类方法的特点、可以达到的预测精度、优缺点和未来太阳能光伏预测方法的发展趋势。本文对太阳能光伏预测的研究具有一定的研究意义。
Abstract: This paper mainly describes the current energy reserve status and future expectations, analyzes the characteristics of solar energy and the significance and value of using solar photovoltaic power generation, and summarizes the previous related research. The current domestic and foreign mainstream solar photovoltaic forecasting methods are classified in detail, and the characteristics of various methods, the prediction accuracy, advantages and disadvantages, and the development trend of solar photovoltaic prediction methods in the future are analyzed. This paper has certain research significance on the prediction of solar photovoltaic.
文章引用：万贝, 姚彦鑫, 黄雅琦. 太阳能光伏发电量预测方法综述[J]. 传感器技术与应用, 2021, 9(1): 1-6. https://doi.org/10.12677/JSTA.2021.91001

参考文献

[1] 钱伯章, 李敏. 能源结构随能源需求增长而持续多样化——2018年世界能源统计年鉴解读[J]. 中国石油和化工经济分析, 2018(8): 51-54.
[2] 周圣尧. 铋和钨酸铋复合光催化固氮体系的构筑及改性研究[D]: [硕士学位论文], 哈尔滨: 哈尔滨工业大学, 2019.
[3] 崔洋, 孙银川, 常倬林. 短期太阳能光伏发电预测方法研究进展[J]. 资源科学, 2013, 35(7): 1474-1481.
[4] Tao, C., Duan, S. and Chen, C. (2010) Forecasting Power Output for Grid-Connected Photovoltaic Power System without Using Solar Radiation Measurement. IEEE International Symposi-um on Power Electronics for Distributed Generation Systems, Hefei, 16-18 June 2010, 773-777. [Google Scholar] [CrossRef] 
[5] 李芬, 陈正洪, 成驰, 等. 太阳能光伏发电量预报方法的发展[J]. 气候变化研究进展, 2011, 7(2): 136-142.
http://dx.chinadoi.cn/10.3969/j.issn.1673-1719.2011.02.010
[6] 段培永, 张洁珏, 崔冲, 等. 太阳能光伏发电功率短期智能预测方法[J]. 山东建筑大学学报, 2016, 31(3): 205-211.
http://dx.chinadoi.cn/10.3969/j.issn.1673-7644.2016.03.001
[7] Charkraborty, S., Weiss, M.D. and Simoes, M.G. (2007) Distributed Intelligent Energy Management System for a Single-Phase High-Frequency AC Micro-Grid. IEEE Transactions on Industrial Electronics, 54, 97-109. [Google Scholar] [CrossRef] 
[8] Mellit, A., Benghanem, M. and Kalogirou, S.A. (2006) An Adap-tive Wavelet-Network Model for Forecasting Daily Total Solar-Radiation. Applied Energy, 83, 705-722. [Google Scholar] [CrossRef] 
[9] Mellit, A. and Pavan, A.M. (2010) A 24-H Forecast of Solar Irradiance Using Artificial Neural Network: Application for Performance Prediction of a Grid-Connected PV Plant at Trieste, Italy. Solar Energy, 84, 807-821. [Google Scholar] [CrossRef] 
[10] Green, M.A. (1982) Solar Cells: Operating Principles, Tech-nology and System Application. Prentice-Hall, New Jersey.
[11] Araujo, G.L.E. and Marti, M. (1982) Determination of the Two-Exponential Solar Cell Equation Parameters from Empirical Data. Solar Cells, 5, 199-204. [Google Scholar] [CrossRef] 
[12] Hammer, A.,Heinemann, D.,Lorenz, E. and Lückehe, B. (1999) Short-Term Forecasting of Solar Radiation: A Statistical Approach Using Satellite Data. Solar Energy, 67, 139-150. [Google Scholar] [CrossRef] 
[13] Lorenz, E., Hurka, J., Heinemann, D. and Georg Beyer, H. (2009) Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems. IEEE Jour-nal of Selected Topics in Applied Earth Observations and Remote Sensing, 2, 2-10. [Google Scholar] [CrossRef] 
[14] 杨超, 榑沼弘贵. 太阳能光伏发电系统发电量的预测方法[J]. 智能建筑电气技术, 2011, 5(2): 29-34.
http://dx.chinadoi.cn/10.3969/j.issn.1729-1275.2011.02.008
[15] Sidrach-de-Cardona, M. and Lopez, L.M. (1998) A Simple Model for Sizing Stand alone Photovoltaic Systems. Solar Energy Materials and Solar Cells, 55, 199-214. [Google Scholar] [CrossRef] 
[16] Kudo, M., Takeuchi, A., Nozaki, Y., Endo, H. and Sumita, J. (2009) Forecasting Electric Power Generation in a Photovoltaic Power System for an Energy Network. Electrical Engi-neering in Japan, 167, 16-23. [Google Scholar] [CrossRef] 
[17] Chowdhury, B.H. and Rahman, S. (1987) Forecasting Sub-Hourly Solar Irradiance for Prediction of Photovoltaic Output. 19th IEEE Photovoltaic Specialists Conference, New Orleans, 4-8 May 1987, 171-176.
[18] 李光明, 刘祖明, 何京鸿, 等. 基于多元线性回归模型的并网光伏发电系统发电量预测研究[J]. 现代电力, 2011, 28(2): 43-48.
http://dx.chinadoi.cn/10.3969/j.issn.1007-2322.2011.02.009
[19] 兰华, 廖志民, 赵阳. 基于ARMA模型的光伏电站出力预测[J]. 电测与仪表, 2011, 48(2): 31-35.
http://dx.chinadoi.cn/10.3969/j.issn.1001-1390.2011.02.007
[20] Safie, F.M. (1989) Probabilistic Modeling of Solar Power Systems. Reliability and Maintainability Symposium, Atlanta, 24-26 January 1989, 425-430. [Google Scholar] [CrossRef] 
[21] Muselli, M., Poggi, P., Notton, G. and Louche, A. (2001) First Order Markov Chain Model for Generating Synthetic “Typical Days” Series of Global Irradiation in Order to Design Photovoltaic Stand alone Systems. Energy Conversion and Management, 42, 675-687. [Google Scholar] [CrossRef] 
[22] 丁明, 徐宁舟. 基于马尔科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, 35(1): 152-157.
[23] Li, Y.Z., Luan, R. and Niu, J.C. (2008) Forecast of Power Generation for Grid-Connected Photovoltaic System Based on Grey Model and Markov Chain. 2018 3rd IEEE Confer-ence on Industrial Electronics and Applications, Singapore, 3-5 June 2008, 1729-1733. [Google Scholar] [CrossRef] 
[24] Aznarte, J.L., Girard, R., Kariniotakis, G., et al. (2008) Short Term Forecasting of Photovoltaic Power Production. DB2: Forecasting Functions with Focus to PV Prediction for Mi-crogrids.
[25] Bracale, A., Caramia, P., Martinis, U.D., et al. (2012) An Improved Bayesian Based Approach for Short Term Photovoltaic Power Forecasting in Smart Grids. International Conference on Renewable Energies and Power Quality, Santiago de Compostela, 28-30 March 2012, 1451-1456.
[26] 栗然, 李广敏. 基于支持向量机回归的光伏发电出力预测[J].中国电力, 2008, 41(2): 74-78.
http://dx.chinadoi.cn/10.3969/j.issn.1004-9649.2008.02.019
[27] Hiyama, T. (1997) Neural Network Based Esti-mation of Maximum Power Generation from PV Module Using Environmental Information. IEEE Transactions on En-ergy Conversion, 12, 241-247. [Google Scholar] [CrossRef] 
[28] Yona, A., Senjyu, T., Saber, A.Y., et al. (2008) Application of Neural Network to 24-Hour-Ahead Generating Power Forecasting for PV System. IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, 20-24 July 2008, 1-6.
[29] Chaouachi, A., Kamel, R.M., Ichikawa, R., et al. (2009) Neural Network Ensemble-Based Solar Power Gen-eration Short-Term Forecasting. World Academy of Science, Engineering and Technology, 54, 54-59.
[30] Charkraborty, S., Weiss, M.D. and Simoes, M.G. (2007) Distributed Intelligent Energy Management System for a Single-Phase High-Frequency AC Micro-Grid. IEEE Transactions on Industrial Electronics, 54, 97-109. [Google Scholar] [CrossRef] 
[31] Ehsan, R.M., Simon, S.P. and Venkateswaran, P.R. (2015) Artifi-cial Neural Network Predictor for Grid-Connected Solar Photovoltaic Installations at Atmospheric Temperature. Interna-tional Conference on Advances in Green Energy, Thiruvananthapuram, 17-18 December 2014, 44-49. [Google Scholar] [CrossRef] 
[32] 韩艳赞, 周伟. 改进BP神经网络的光伏系统发电功率预测[J]. 计算机系统应用, 2016, 25(11):227-231.
http://dx.chinadoi.cn/10.15888/j.cnki.csa.005582
[33] 韩伟, 王宏华, 杜炜. 基于FOA-Elman神经网络的光伏电站短期出力预测模型[J]. 电测与仪表, 2014, 51(12): 120-124.
http://dx.chinadoi.cn/10.3969/j.issn.1001-1390.2014.12.023
[34] 陈昌松, 段善旭, 殷进军. 基于神经网络的光伏阵列发电预测模型的设计[J]. 电工技术学报, 2009, 24(9): 153-158.
http://dx.chinadoi.cn/10.3321/j.issn:1000-6753.2009.09.023

为你推荐