Potential Solar Power Generation Dataset at 0.25° Resolution in the Belt and Road and Surround Region (2015)
ZHANG Qian1XIN Xiaozhou1ZHANG Hailong1GONG Wei1LI Li1LIU Qinhuo1
1 State Key Laboratory of Remote Sensing Science，Institute of Remote Sensing and Digital Earth，Chinese Academy of Science，Beijing 100101，China
DOI:10.3974/geodb.2017.03.17.V1
Published:Oct. 2017
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Key Words：
The Belt and Road,solar energy,power generation potential,light energy resource
Abstract：
Potential solar power generation in the Belt and Road and surround region is the potential energy and resources. The solar radiation, surface cover, terrain, GLCNMO (Version 1) 30” land cover products were used to identify the land cover types in order to calculate the available area. SRTM3 elevation data was used to extract the area which was restricted by slope. MuSyQ (Multi-source data Synergized Quantitative remote sensing production system) radiation products in 2015 were to obtain the surface incident solar radiation in the whole year. Combined with the Multi-criteria evaluation model, the potential solar power generation dataset at 0.25° resolution in the B&R and surround region was developed. The data result shows that there are the greatest potential for solar power generation, up to 400KWh/m² in the Arabian Peninsula, the Iranian Plateau and the African Sahara Desert. The resolution of the dataset is 0.25°. The dataset was archived in .tif data format with the compressed data size of 3.75MB.Browse
Foundation Item：
National Natural Science Foundation of China (41201352)
Data Citation：
ZHANG Qian, XIN Xiaozhou, ZHANG Hailong, GONG Wei, LI Li, LIU Qinhuo.Potential Solar Power Generation Dataset at 0.25° Resolution in the Belt and Road and Surround Region (2015)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2017. https://doi.org/10.3974/geodb.2017.03.17.V1.
Xin, X. Z., Zhang, Q., Zhang, H. L., et al. Potential solar power dataset in 0.25° grid in the Belt and Road and surround region (2015) [J]. Journal of Global Change Data & Discovery, 2017, 1(4): 408–413. DOI: 10.3974/geodp.2017.04.04.
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| 0 | Datapaper_B&RSolarPower.pdf | 859.00kb | DownLoad |
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B&RSolarPower.tif |
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