Collect ｜ Homepage Advertisement ｜ Contact us ｜ Message
Adv. search

Arid Land Geography >

2023 , Vol. 46 >Issue 1: 47 - 55

DOI: https://doi.org/10.12118/j.issn.1000-6060.2022.259

Climatology and Hydrology

Construction and validation of the R models for short-term solar irradiance forecasting

  • Degui YAO ,
  • Weijia LIU ,
  • Yongxiang HAN ,
  • Zhe LI ,
  • Yun LIANG
Expand
  • 1. Electric Power Research Institute, Electric Power of Henan, Zhengzhou 450000, Henan, China
    2. Hangzhou Meteorological Bureau, Hangzhou 310051, Zhejiang, China
    3. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China

Received date: 2022-06-01

  Revised date: 2022-07-17

  Online published: 2023-02-21

Fold

Abstract

Improving the forecasting accuracy of solar irradiance, as an important safeguard ensuring the stability of grid operations, remains one of the challenging technical bottlenecks. This study constructs a short-time solar irradiance forecasting model (R model) based on the cloud relative radiative forcing ratio R derived by the ground-based solar irradiances from the relationship between clouds and radiation and then evaluates the forecasting accuracy of the R model using 16-year solar irradiance measurements at the Southern Great Plains Central Facility site in the United States. The results show that (1) the R model significantly outperforms the simple persistence model and exhibits a 2%-25% improvement relative to the advanced smart persistence model (also called cloud relative radiative forcing (RCRF) model herein) in the cloudy case. (2) For the overall validation with 2.9×105 individual cloudy cases in 8 categories over 16 years, the forecast accuracy of the R model is significantly better than that of the simple model and the RCRF model at lead times longer than 1 h. Compared with the RCRF model, the forecast performance of the R model is improved by 25% and 19% in global horizontal irradiance and direct normal irradiance, respectively, and the lead time is extended by 1.5 and 1 h. (3) The R model with higher forecast performance sets a higher standard for the benchmark model of short-term solar irradiance forecasting. Meanwhile, the R model only requires short-term ground-based radiation observations for forecasting, facilitating and providing new possibilities for photovoltaic power plants without concurrent meteorological measurements in short-term solar irradiance forecasting.

Key words： persistence model; short-term irradiance forecast; forecasting performance; photovoltaic power plant

Cite this article

Degui YAO , Weijia LIU , Yongxiang HAN , Zhe LI , Yun LIANG . Construction and validation of the R models for short-term solar irradiance forecasting[J]. Arid Land Geography, 2023 , 46(1) : 47 -55 . DOI: 10.12118/j.issn.1000-6060.2022.259

References

[1] 胡鞍钢. 中国实现2030年前碳达峰目标及主要途径[J]. 北京工业大学学报(社会科学版), 2021, 21(3): 1-15.
[1] [Hu Angang. China’s goal of achieving carbon peak by 2030 and its main approaches[J]. Journal of Beijing University of Technology (Social Sciences Edition), 2021, 21(3): 1-15.]
[2] 邹才能, 熊波, 薛华庆, 等. 新能源在碳中和中的地位与作用[J]. 石油勘探与开发, 2021, 48(2): 411-420.
[2] [Zou Caineng, Xiong Bo, Xue Huaqing, et al. The role of new energy in carbon neutral[J]. Petroleum Exploration and Development, 2021, 48(2): 411-420.]
[3] 崔容强. 太阳能光伏发电——中国低碳经济的希望[J]. 自然杂志, 2010, 32(3): 149-154.
[3] [Cui Rongqiang. Solar photovoltaic power: Hope of low-carbon economy in China[J]. Chinese Journal of Nature, 2010, 32(3): 149-154.]
[4] 刘佳, 何清, 刘蕊, 等. 新疆太阳辐射特征及其太阳能资源状况[J]. 干旱气象, 2008, 26(4): 61-66.
[4] [Liu Jia, He Qing, Liu Rui, et al. Solar radiation character and solar energy resource in Xinjiang[J]. Arid Meteorology, 2008, 26(4): 61-66.]
[5] 桑建人, 刘玉兰, 林莉. 宁夏太阳辐射特征及太阳能利用潜力综合评价[J]. 中国沙漠, 2006, 26(1): 122-125.
[5] [Sang Jianren, Liu Yulan, Lin Li. Characteristic of solar radiation in Ningxia and integrated evaluation on utilization potential of solar energy[J]. Journal of Desert Research, 2006, 26(1): 122-125.]
[6] Fouad M M, Shihata L A, Morgan E I. An integrated review of factors influencing the performance of photovoltaic panels[J]. Renewable and Sustainable Energy Reviews, 2017, 80: 1499-1511.
[7] 张雪莉, 刘其辉, 马会萌, 等. 光伏电站输出功率影响因素分析[J]. 电网与清洁能源, 2012, 28(5): 75-81.
[7] [Zhang Xueli, Liu Qihui, Ma Huimeng, et al. Analysis of influencing factors of output power of photovoltaic power plant[J]. Advances of Power System & Hydroelectric Engineering, 2012, 28(5): 75-81.]
[8] Alsharif M H, Younes M K, Kim J. Time series ARIMA model for prediction of daily and monthly average global solar radiation: The case study of Seoul, South Korea[J]. Symmetry, 2019, 11(2): 240, doi: 10.3390/sym11020240.
[9] Jimenez P A, Hacker J P, Dudhia J, et al. WRF-Solar: Description and clear-sky assessment of an augmented NWP model for solar power prediction[J]. Bulletin of the American Meteorological Society, 2016, 97(7): 1249-1264.
[10] Diagne M, David M, Lauret P, et al. Review of solar irradiance forecasting methods and a proposition for small-scale insular grids[J]. Renewable and Sustainable Energy Reviews, 2013, 27: 65-76.
[11] Aggarwal S K, Saini L M. Solar energy prediction using linear and non-linear regularization models: A study on AMS (American Meteorological Society) 2013-14 solar energy prediction contest[J]. Energy, 2014, 78: 247-256.
[12] Voyant C, Notton G, Kalogirou S, et al. Machine learning methods for solar radiation forecasting: A review[J]. Renewable Energy, 2017, 105: 569-582.
[13] 马金玉, 罗勇, 申彦波, 等. 太阳能预报方法及其应用和问题[J]. 资源科学, 2011, 33(5): 829-837.
[13] [Ma Jinyu, Luo Yong, Shen Yanbo, et al. A review on methods of solar energy forecasting and its application[J]. Resources Science, 2011, 33(5): 829-837.]
[14] 蒋俊霞, 高晓清, 吕清泉. 基于地基云图的云跟踪与太阳辐照度超短期预报方法研究[J]. 太阳能学报 2020, 41(5): 351-358.
[14] [Jiang Junxia, Gao Xiaoqing, Lü Qingquan. Study on cloud tracking and solar irradiance ultra-short-term forecasting based on TSI images[J]. Acta Energiae Solaris Sinica, 2020, 41(5): 351-358.]
[15] Lara-Fanego V, Ruiz-Arias J A, Pozo-Vázquez D, et al. Evaluation of the WRF model solar irradiance forecasts in Andalusia (southern Spain)[J]. Solar Energy, 2012, 86(8): 2200-2217.
[16] 赵东. 中国太阳能长期变化及计算方法研究[D]. 南京: 南京信息工程大学, 2009.
[16] [Zhao Dong. Long term changes and assessment models of solar energy over China[D]. Nanjing: Nanjing University of Information Science and Technology, 2009.]
[17] Liu B Y H, Jordan R C. The interrelationship and of direct, diffuse and characteristic distribution total solar radiation[J]. Solar Energy, 1960, 4(3): 1-19.
[18] Kumler A, Xie Y, Zhang Y. A physics-based smart persistence model for intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique[J]. Solar Energy, 2019, 177: 494-500.
[19] 刘唯佳. 基于云参量的太阳辐射短时临近预报模型的构建及预报评估[D]. 南京: 南京信息工程大学, 2021.
[19] [Liu Weijia. Construction and evaluation of cloud-property based solar radiation short-term forecast systems[D]. Nanjing: Nanjing University of Information Science and Technology, 2021.]
[20] Xie Y, Liu Y. A new approach for simultaneously retrieving cloud albedo and cloud fraction from surface-based shortwave radiation measurements[J]. Environmental Research Letters, 2013, 8(4): 044023, doi: 10.1088/1748-9326/8/4/044023.
[21] Long C N, Gaustad K L. The shortwave (SW) clear-sky detection and fitting algorithm: Algorithm operational details and explanations[R]. United States: DOE Office of Science Atmospheric Radiation Measurement (ARM) User Facility, 2004.
[22] Liu Y, Wu W, Jensen M P, et al. Relationship between cloud radiative forcing, cloud fraction and cloud albedo, and new surface-based approach for determining cloud albedo[J]. Atmospheric Chemistry and Physics, 2011, 11(14): 7155-7170.
[23] Reda I, Andreas A. Solar position algorithm for solar radiation applications[J]. Solar Energy, 2004, 76(5): 577-589.
[24] Yang D, Alessandrini S, Antonanzas J, et al. Verification of deterministic solar forecasts[J]. Solar Energy, 2020, 210: 20-37.
[25] 于志翔, 李霞, 于晓晶, 等. 2003—2019年新疆气溶胶光学厚度时空变化特征[J]. 干旱区地理, 2022, 45(2): 346-358.
[25] [Yu Zhixiang, Li Xia, Yu Xiaojing, et al. Spatiotemporal variation characteristics of aerosol optical depth in Xinjiang from 2003 to 2019[J]. Arid Land Geography, 2022, 45(2): 346-358.]
[26] 孙小雲, 房彦杰, 赵景峰, 等. 塔克拉玛干沙漠输沙势时空分布特征[J]. 干旱区地理, 2020, 43(1): 38-47.
[26] [Sun Xiaoyun, Fang Yanjie, Zhao Jingfeng, et al. Spatial and temporal distribution characteristics of sand drift potential in Taklimakan Desert[J]. Arid Land Geography, 2020, 43(1): 38-47.]
[27] 刘畅, 李诚志, 李胜辉, 等. 基于栅格累加法的塔里木盆地沙漠化分析[J]. 干旱区地理, 2021, 44(1): 197-207.
[27] [Liu Chang, Li Chengzhi, Li Shenghui, et al. Desertification analysis based on grid accumulation method in Tarim Basin, China[J]. Arid Land Geography, 2021, 44(1): 197-207.]
Options
Outlines

/

〈 〉