VALIDATION AND APPLICABILITY OF MULTI-SOURCE RADIATION DATA FOR REFINED SOLAR RESOURCE ASSESSMENT

Fan Jing; Shen Yanbo; Wang Tingting; Hu Yueming; Jia Beixi

doi:10.19912/j.0254-0096.tynxb.2023-2057

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 226-234. DOI: 10.19912/j.0254-0096.tynxb.2023-2057

VALIDATION AND APPLICABILITY OF MULTI-SOURCE RADIATION DATA FOR REFINED SOLAR RESOURCE ASSESSMENT

Fan Jing¹, Shen Yanbo^2,3, Wang Tingting⁴, Hu Yueming², Jia Beixi^2,3

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Abstract

This study evaluates the accuracy of three widely used solar radiation datasets（NASA, Solargis, and Meteonorm）for solar resource assessment in China. The evaluation is conducted by comparing their monthly global horizontal irradiance （GHI） estimates against high-quality ground-based measurements from 99 meteorological stations across China. A comprehensive index-based evaluation framework was established to identify the optimal datasets for each region and derive corresponding error correction coefficients. The results show that the spatial distributions of GHI from Solargis and Meteonorm align more closely with observations than those from NASA. The most significant discrepancies are observed in Northwest China, Xizang and Sichuan Province. Error analysis indicates that Meteonorm achieves the highest correlation coefficient （R）（R ≥ 0.98 at 75% of the stations） and the smallest error accounting for all error metrics, including root mean square error, mean absolute error, and mean relative error. Solargis demonstrates moderate accuracy, while NASA exhibits the largest error. A significant correlation is identified between latitude and mean relative error. Solargis tends to overestimate GHI at lower-latitude and underestimate at higher latitudes, whereas Meteonorm displays the opposite trend. Based on the results of error analysis, this study constructs a comprehensive index evaluation method, screens out the optimal data types of 99 stations, and spatially interpolates them to the national scale. The findings suggest that Meteonorm is preferred in 55% of China’s regions, followed by Solargis in 27% and NASA in 18%. The mean relative error of the optimized dataset is confined to a range of -4.5% to 5.9%. To improve the accuracy of solar energy assessments, the following calibration is recommended: GHI values should be increased by 1%-5% in northern regions, Xizang, and western Sichuan, and decreased by 1%-6% in southern Xinjiang, Qinghai, Central China, and South China.

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solar energy / resource assessment / radiation / accuracy validation / applicability / error correction

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Fan Jing, Shen Yanbo, Wang Tingting, Hu Yueming, Jia Beixi. VALIDATION AND APPLICABILITY OF MULTI-SOURCE RADIATION DATA FOR REFINED SOLAR RESOURCE ASSESSMENT[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 226-234 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2057

References

[1] IPCC. Climate change 2023: Synthesis report[M]. Cambridge: Cambridge University Press, 2023.
[2] 陆春晖, 袁佳双, 黄磊, 等. 从IPCC看全球盘点中的关键科学问题及其对中国的启示[J]. 气候变化研究进展, 2024, 20(6): 736-746.
LU C H, YUAN J S, HUANG L, et al.Key scientific issues in the global stocktake from the perspective of IPCC and their implications for China[J]. Climate change research, 2024, 20(6): 736-746.
[3] 姚玉璧, 郑绍忠, 杨扬, 等. 中国太阳能资源评估及其利用效率研究进展与展望[J]. 太阳能学报, 2022, 43(10): 524-535.
YAO Y B, ZHENG S Z, YANG Y, et al.Progress and prospects on solar energy resource evaluation and utilization efficiency in China[J]. Acta energiae solaris sinica, 2022, 43(10): 524-535.
[4] 2024年全国电力工业统计数据[R].2024年全国电力工业统计数据[R].北京:国家能源局,2024.
2024 national power industry statistical data[R]. Beijing: National Energy Administration (NEA), 2024.
[5] 袁云. 谈太阳能资源评估及其对项目开发的重要性[J]. 环境与发展, 2017, 29(6): 17-18.
YUAN Y.On the evaluation of solar energy resources and its importance for project development[J]. Environment and development, 2017, 29(6): 17-18.
[6] 王科, 黄晶. 国内外太阳能资源评估方法研究现状和展望[J]. 气候变化研究进展, 2023, 19(2): 160-172.
WANG K, HUANG J.Domestic and abroad research status and prospects of solar energy resource evaluation methods[J]. Climate change research, 2023, 19(2): 160-172.
[7] 申彦波. 我国太阳能资源评估方法研究进展[J]. 气象科技进展, 2017, 7(1): 77-84.
SHEN Y B.Research progress on solar energy resource assessment methods in China[J]. Advances in meteorological science and technology, 2017, 7(1): 77-84.
[8] 陈阵, 李令, 于良. 后平价时代集中式光伏项目开发造价的主要影响因素及控制[J]. 四川水力发电, 2022,41(6): 112-115.
CHEN Z, LI L, YU L.Main influencing factors and cost control measures of centralized PV project development in the post-parity era[J]. Sichuan hydroelectric power, 2022,41(6): 112-115.
[9] 谢国辉, 李娜娜, 汪晓露, 等. 光伏电站基地技术和经济可开发量评估的方法和模型[J]. 电器与能效管理技术, 2019(6): 42-47.
XIE G H, LI N N, WANG X L, et al.Research on evaluating model of technical and economic developable amount of photovoltaic power station bases[J]. Electrical and energy efficiency management technology, 2019(6): 42-47.
[10] 张双益, 李熙晨. ERA5资料应用于中国地区太阳能资源评估研究[J]. 太阳能学报, 2023, 44(5): 280-285.
ZHANG S Y, LI X C.Study on application of ERA5 data to solar energy resource assessment over China's region[J]. Acta energiae solaris sinica, 2023, 44(5): 280-285.
[11] YANG D, WANG W, XIA X A.A concise overview on solar resource assessment and forecasting[J]. Advances in atmospheric sciences, 2022,39(12): 1-13.
[12] 李英姿, 李智. 光伏发电项目太阳能辐射量数据对比与选用[J]. 建筑电气, 2016, 35(4): 34-40.
LI Y Z, LI Z.Comparison and selection of solar radiation data for photovoltaic power generation projects[J]. Building electricity, 2016, 35(4): 34-40.
[13] 马金玉, 梁宏, 罗勇, 等. 中国近50年太阳直接辐射和散射辐射变化趋势特征[J]. 物理学报, 2011, 60(6): 853-866.
MA J Y, LIANG H, LUO Y, et al.Variation trend of direct and diffuse radiation in China over recent 50 years[J]. Acta physica sinica, 2011, 60(6): 853-866.
[14] 李柯, 何凡能. 中国陆地太阳能资源开发潜力区域分析[J]. 地理科学进展, 2010, 29(9): 1049-1054.
LI K, HE F N.Analysis on mainland China’s solar energy distribution and potential to utilize solar energy as an alternative energy source[J]. Progress in geography, 2010, 29(9): 1049-1054.
[15] 权继梅, 杨云, 丁蕾. 中国气象辐射观测站总辐射表稳定性统计分析[J]. 气象科技, 2018, 46(2): 224-228.
QUAN J M, YANG Y, DING L.Statistical analysis of stability of total radiation meters at national meteorological radiation observation stations[J]. Meteorological science and technology, 2018, 46(2): 224-228.
[16] 王娟敏, 孙娴, 孙睿, 等. 2000—2016年中国再分析辐射资料与观测值对比[J]. 热带气象学报, 2020, 36(6): 734-743.
WANG J M, SUN X, SUN R, et al.Comparison of reanalysis radiation data and observations in China for 2000—2016[J]. Journal of tropical meteorology, 2020, 36(6): 734-743.
[17] CHANDLER W, WHITLOCK C, STACKHOUSE P.NASA climatological data for renewable energy assessment[J]. Journal of solar energy engineering, 2004, 126(4): 945-949.
[18] Handbook part Ⅱ: theory version 8.0[R]. Zurich: Global Meteorological Database Version 8, version 8.0[R]. Zurich: Global Meteorological Database Version 8, September 2020.
[19] KLEISSL J.Solar energy forecasting and resource assessment[M]. Amsterdam: Elsevier, 2013. DOI:10.1016/c2011-0-07022-9.
[20] CEBECAUER T, SURI M.Typical meteorological year data: SolarGIS approach[J]. Energy procedia, 2015, 69: 1958-1969.
[21] 田启明, 许和明, 袁亚洲, 等. 光热电站太阳能资源典型年选取方法及验证的探讨[J]. 电力勘测设计, 2020(8): 61-65.
TIAN Q M, XU H M, YUAN Y Z, et al.Discussion on the method selection and verification for typical year of solar energy resources in solar thermal power plant[J]. Electric power survey & design, 2020(8): 61-65.
[22] GB/T 34325—2017, 太阳能资源数据准确性评判方法[S].
GB/T 34325—2017, Method for evaluating the accuracy of solar energy resource data[S].
[23] 李新, 程国栋, 卢玲. 空间内插方法比较[J]. 地球科学进展, 2000, 15(3): 260-265.
LI X, CHENG G D, LU L.Comparison of spatial interpolation methods[J]. Advances in earth science, 2000, 15(3): 260-265.
[24] 邓晓斌. 基于ArcGIS两种空间插值方法的比较[J]. 地理空间信息, 2008, 6(6): 85-87.
DENG X B.Comparison between two space interpolation methods based on ArcGIS[J]. Geospatial information, 2008, 6(6): 85-87.
[25] GB/T 37526—2019, 太阳能资源评估方法[S].
GB/T 37526—2019, Method for evaluating solar energy resources[S].
[26] 齐月, 房世波, 周文佐. 近50年来中国地面太阳辐射变化及其空间分布[J]. 生态学报, 2014, 34(24): 7444-7453.
QI Y, FANG S B, ZHOU W Z.Variation and spatial distribution of surface solar radiation in China over recent 50 years[J]. Acta ecologica sinica, 2014, 34(24): 7444-7453.
[27] 王传辉, 申彦波, 姚锦烽, 等. 3种再分析资料在太阳能资源评估中的适用性[J]. 太阳能学报, 2022, 43(8): 164-173.
WANG C H, SHEN Y B, YAO J F, et al.Applicability of three reanalysis data in assessment of solar energy resources in China[J]. Acta energiae solaris sinica, 2022, 43(8): 164-173.