以全国99个地面气象站观测的水平面月总辐照量（GHI）为基准,对比分析台站实测GHI与NASA、Solargis、Meteonorm这3类数据的差异,构建综合指数评价方法,以筛选太阳能资源评估中的最优数据源,并给出误差订正系数。结果表明,Solargis、Meteonorm数据单站GHI的空间分布与实测值更为接近,NASA数据在四川盆地存在明显高估;3类数据的站点GHI在西北、西藏、四川等地空间差异最显著。误差分析显示,Meteonorm数据与实测值的相关系数最高,75%的站点相关系数≥0.98,且均方根误差、绝对误差、相对误差最小的站点数量占比最多,Solargis数据精度次之,NASA数据误差最大。纬度与相对误差的相关分析表明,Solargis数据在低纬地区高估GHI,高纬地区低估GHI,Meteonorm则呈现相反趋势。基于误差分析结果,本研究构建综合指数评价方法,筛选出99站最优数据类型,并将其空间插值至全国范围。结果显示,全国55%的区域优选Meteonorm数据,27%的区域优选Solargis数据,18%的区域优选NASA数据,优选后的最优数据相对误差范围缩小至-4.5%~5.9%。因此,在太阳能资源评估应用中,建议北方大部、西藏、川西等地应用最优数据时,将GHI适当增加1%~5%,新疆南部、青海、华中、华南等地则需将GHI减少1%~6%。
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.
关键词
太阳能 /
资源评估 /
辐射 /
精度检验 /
适用性 /
误差订正
Key words
solar energy /
resource assessment /
radiation /
accuracy validation /
applicability /
error correction
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基金
中国气象局气候资源经济转化重点实验室开放课题（2023008）
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