数据集(库)目录

出版期刊|区域分类

2021年第12期
2019年第02期
数据详情

中国太阳辐射两种区划方案数据集


姜侯
中国科学院地理科学与资源研究所,北京100101

DOI:10.3974/geodb.2023.07.07.V1

出版时间:2023年7月

网页浏览次数:4832       数据下载次数:58      
数据下载量:115.36 MB      数据DOI引用次数:

关键词:

太阳辐射,分区边界,高斯混合模型,太阳能利用

摘要:

太阳辐射分区是指导太阳能利用和制定区域发展规划的基础。作者利用基于高斯混合模型的自动分区算法进行中国区域太阳辐射分区,其中分区数目由贝叶斯推理自适应推断。分区算法使用2007-2020年中国地面太阳辐射观测数据进行拟合,然后将对应时间段的遥感反演产品带入拟合后的模型,确定分层分区的精细边界。分区结果采用基于中国716个气象站点的日照时数重构的高精度太阳辐射数据进行验证。验证结果表明,分区算法可以将具有不同太阳辐射特性的站点划分到特定区域,准确率约为90%。此外,大多数不准确的站点位于区域的内部而不是边缘,这进一步证明了所识别的区域边界的可靠性。本数据集内容包括:(1)能反映中国太阳辐射总量差异的五分区方案数据;(2)能反映中国太阳辐射总量与季节性差异的十分区方案数据。数据集存储为.shp格式,一共由14个文件组成,数据量为3.10 MB (压缩为1个文件,1.98 MB)。数据论文

基金项目:

遥感科学国家重点实验室(OFSLRSS202204);国家自然科学基金(42201382)

数据引用方式:

姜侯. 中国太阳辐射两种区划方案数据集[J/DB/OL]. 全球变化数据仓储电子杂志(中英文), 2023. https://doi.org/10.3974/geodb.2023.07.07.V1.

姜侯, 姚凌. 中国太阳辐射分层分区边界数据集的研发[J]. 全球变化数据学报(中英文), 2023, 7(2): 185-194.

参考文献:

[1] Kabir, E., Kumar, P., Kumar, S., et al. Solar energy: Potential and future prospects [J]. Renewable and Sustainable Energy Reviews, 2018, 82: 894-900.
     [2] Zhang, Y., Ren, J., Pu, Y., et al. Solar energy potential assessment: A framework to integrate geographic, technological, and economic indices for a potential analysis [J]. Renewable Energy, 2020, 149: 577-586.
     [3] Bódis, K., Kougias, I., Jäger-Waldau, A., et al. A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union [J]. Renewable and Sustainable Energy Reviews, 2019, 114: 109309.
     [4] Sweerts, B., Pfenninger, S., Yang, S., et al. Estimation of losses in solar energy production from air pollution in China since 1960 using surface radiation data [J]. Nature Energy, 2019, 4: 657-663.
     [5] Mensour, O. N., El Ghazzani, B., Hlimi, B., et al. A geographical information system-based multi-criteria method for the evaluation of solar farms locations: A case study in Souss-Massa area, southern Morocco [J]. Energy, 2019, 182: 900-919.
     [6] 蔺阿琳. 城市太阳能可利用空间评估与规划研究——以哈尔滨为例[D]. 哈尔滨: 哈尔滨工业大学, 2020.
     [7] 陈洁, 罗智星, 杨柳. 建筑节能设计用于干热气候区气候特征分析[J]. 城市建筑, 2019, 16: 48-51.
     [8] Bai, L., Wang, S. Definition of new thermal climate zones for building energy efficiency response to the climate change during the past decades in China [J]. Energy, 2019, 170: 709-719.
     [9] Walsh, A., Cóstola, D., Labaki, L. C. Review of methods for climatic zoning for building energy efficiency programs [J]. Building and Environment, 2017, 112: 337-350.
     [10] Wan, K. K. W., Li, D. H. W., Yang, L., et al. Climate classifications and building energy use implications in China [J]. Energy and Buildings, 2010, 42: 1463-1471.
     [11] 王炳忠. 中国太阳能资源利用区划[J]. 太阳能学报, 1983, 4(3): 221-228.
     [12] Al-Azri, N. A., Zurigat, Y. H., Al-Rawahi, N. Z. Development of bioclimatic chart for passive building design [J]. International Journal of Sustainable Energy, 2013, 32: 713-723.
     [13] 周扬, 吴文祥, 胡莹等. 西北地区太阳能资源空间分布特征及资源潜力评估[J]. 自然资源学报, 2010, 25: 1738-1749.
     [14] Lau, C., C. S., Lam, J. C., Yang, L. Climate classification and passive solar design implications in China [J]. Energy Conversion and Management, 2007, 48: 2006-2015.
     [15] Liu, Y. F., Zhou, Y., Wang, D. J., et al. Classification of solar radiation zones and general models for estimating the daily global solar radiation on horizontal surfaces in China [J]. Energy Conversion and Management, 2017, 154: 168-179.
     [16] Pinker, R. T., Laszlo, I. Modeling surface solar irradiance for datellite spplications on a global scale [J]. Journal of Applied Meteorology, 1992, 31: 194-211.
     [17] Kato, S., Loeb, N. G., Rose, F. G., et al. Surface irradiances sonsistent with CERES-derived top-of-atmosphere shortwave and longwave irradiances [J]. Joural of Climate, 2013, 26: 2719-2740.
     [18] Zhang, X. T., Liang, S. L., Zhou, G. Q., et al. Generating Global LAnd Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data [J]. Remote Sensing of Environment, 2014, 152: 318-332.
     [19] Tang, W., Yang, K., Qin, J., et al. A 16-year dataset (2000–2015) of high-resolution (3h, 10km) global surface solar radiation [J]. Earth System Science Data, 2019, 11: 1905-1915.
     [20] Antoniak, C. E. Mixtures of Dirichlet processes with spplications to Bayesian nonparametric Problems [J]. The Annals of Statistics, 1974, 2: 1152-1174.
     [21] Frimane, Â., Aggour, M., Ouhammou, B., et al. A Dirichlet-multinomial mixture model-based approach for daily solar radiation classification [J]. Solar Energy, 2018, 171: 31-39.
     [22] Soubdhan, T., Emilion, R., Calif, R. Classification of daily solar radiation distributions using a mixture of Dirichlet distributions [J]. Solar Energy, 2009, 83: 1056-1063.
     

数据下载:

序号 数据名 数据大小 操作
0Datapaper_1.pdf3247.00kb下载
1 SolarRadiationZones.rar 2036.74KB
主管单位