Amillo, A. G., T. Huld, and R. Müller, 2014: A new database of global and direct aolar radiation using the eastern meteosat satellite, models and validation. Remote Sensing, 6, 8165−8189, https://doi.org/10.3390/rs6098165.
Antonanzas, J., N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de-Pison, and F. Antonanzas-Torres, 2016: Review of photovoltaic power forecasting. Solar Energy, 136, 78−111, https://doi.org/10.1016/j.solener.2016.06.069.
Antonanzas-Torres, F., R. Urraca, J. Polo, O. Perpiñán-Lamigueiro, and R. Escobar, 2019: Clear sky solar irradiance models: A review of seventy models. Renewable and Sustainable Energy Reviews, 107, 374−387, https://doi.org/10.1016/j.rser.2019.02.032.
Arbizu-Barrena, C., J. A. Ruiz-Arias, F. J. Rodríguez-Benítez, D. Pozo-Vázquez, and J. Tovar-Pescador, 2017: Short-term solar radiation forecasting by advecting and diffusing MSG cloud index. Solar Energy, 155, 1092−1103, https://doi.org/10.1016/j.solener.2017.07.045.
Bai, B., Y. H. Wang, C. Fang, S. Q. Xiong, and X. M. Ma, 2021: Efficient deployment of solar photovoltaic stations in China: An economic and environmental perspective. Energy, 221, 119834, https://doi.org/10.1016/j.energy.2021.119834.
Beyer, H. G., J. P. Martinez, M. Suri, J. L. Torres, E. Lorenz, S. C. Müller, C. Hoyer-Klick, and P. Ineichen, 2009: D 1.1.3 Report on Benchmarking of Radiation Products. Management and Exploitation of Solar Resource Knowledge. Available from http://www.mesor.org/docs/MESoR_Benchmarking_of_radiation_products.pdf.
Burandt, T., B. Xiong, K. Löffler, and P.-Y. Oei, 2019: Decarbonizing China’s energy system – Modeling the transformation of the electricity, transportation, heat, and industrial sectors. Applied Energy, 255, 113820, https://doi.org/10.1016/j.apenergy.2019.113820.
Chen, X. M., Y. Li, and R. Z. Wang, 2020: Performance study of affine transformation and the advanced clear-sky model to improve intra-day solar forecasts. Journal of Renewable and Sustainable Energy, 12, 043703, https://doi.org/10.1063/5.0009155.
Cros, S., M. Albuisson, M. Lefèvre, C. Rigollier, and L. Wald, 2004: HelioClim: A long-term database on solar radiation for Europe and Africa. Proceedings of Eurosun 2004, Freiburg, Germany, PSE GmbH.
Cros, S., N. Sébastien, O. Liandrat, and N. Schmutz, 2014: Cloud pattern prediction from geostationary meteorological satellite images for solar energy forecasting. Proceedings of SPIE 9242, Remote Sensing of Clouds and the Atmosphere XIX; and Optics in Atmospheric Propagation and Adaptive Systems XVII, Amsterdam, Netherlands, SPIE,
Damiani, A., and Coauthors, 2018: Evaluation of Himawari-8 surface downwelling solar radiation by ground-based measurements. Atmospheric Measurement Techniques, 11, 2501−2521, https://doi.org/10.5194/amt-11-2501-2018.
Gallucci, D., and Coauthors,, 2018: Nowcasting surface solar irradiance with AMESIS via motion vector fields of MSG-SEVIRI Data. Remote Sensing, 10, 845, https://doi.org/10.3390/rs10060845.
Gueymard, C. A., 2008: REST2: High-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation – Validation with a benchmark dataset. Solar Energy, 82, 272−285, https://doi.org/10.1016/j.solener.2007.04.008.
Gueymard, C. A., and R. George, 2005: Gridded aerosol optical depth climatological datasets over continents for solar radiation modeling. Proceedings of Solar World Congress, Orlando, USA, International Solar Energy Society. [Available online from https://www.semanticscholar.org/paper/GRIDDED-AEROSOL-OPTICAL-DEPTH-CLIMATOLOGICAL-OVER-Gueymard-George/a3e7dad6035e6a35afdccf9bf4b98319436c3014]
Hammer, A., D. Heinemann, E. Lorenz, and B. Lückehe, 1999: Short-term forecasting of solar radiation: A statistical approach using satellite data. Solar Energy, 67, 139−150, https://doi.org/10.1016/S0038-092X(00)00038-4.
Huang, C. L., J. Z. Li, W. W. Sun, Q. X. Chen, Q.-J. Mao, and Y. Yuan, 2021: Long-term variation assessment of aerosol load and dominant types over Asia for air quality studies using multi-sources aerosol datasets. Remote Sensing, 13, 3116, https://doi.org/10.3390/rs13163116.
Huang, G. H., Z. Q. Li, X. Li, S. L. Liang, K. Yang, D. D. Wang, and Y. Zhang, 2019: Estimating surface solar irradiance from satellites: Past, present, and future perspectives. Remote Sensing of Environment, 233, 111371, https://doi.org/10.1016/j.rse.2019.111371.
IRENA, 2020: Renewable Capacity Statistics 2020: International Renewable Energy Agency (IRENA), Abu Dhabi. [Available online from https://irena.org/publications/2020/Mar/Renewable-Capacity-Statistics-2020]
Jia, D. Y., J. J. Hua, L. P. Wang, Y. T. Guo, H. Guo, P. P. Wu, M. Liu, and L. W. Yang, 2021: Estimations of global horizontal irradiance and direct normal irradiance by using Fengyun-4A satellite data in northern China. Remote Sensing, 13, 790, https://doi.org/10.3390/rs13040790.
Jiang, H., N. Lu, J. Qin, W. J. Tang, and L. Yao, 2019: A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data. Renewable and Sustainable Energy Reviews, 114, 109327, https://doi.org/10.1016/j.rser.2019.109327.
Kallio-Myers, V., A. Riihelä, P. Lahtinen, and A. Lindfors, 2020: Global horizontal irradiance forecast for Finland based on geostationary weather satellite data. Solar Energy, 198, 68−80, https://doi.org/10.1016/j.solener.2020.01.008.
Kleissl, J., 2013: Solar Energy Forecasting and Resource Assessment. Academic Press,
Lamsal, D., V. Sreeram, Y. Mishra, and D. Kumar, 2018: Kalman filter approach for dispatching and attenuating the power fluctuation of wind and photovoltaic power generating systems. IET Generation, Transmission & Distribution, 12, 1501−1508, https://doi.org/10.1049/iet-gtd.2017.0663.
Letu, H., T. Y. Nakajima, T.X. Wang, H. Z. Shang, R. Ma, K. Yang, A. J. Baran, J. Riedi, H. Ishimoto, M. Yoshida, C. Shi, P. Khatri, Y. H. Du, L. f. Chen, and J. C Shi, 2021: A new benchmark for surface radiation products over the East Asia-Pacific region retrieved from the Himawari-8/AHI next-generation geostationary satellite. Bull. Amer. Meteor. Soc, 103, E873−888, https://doi.org/10.1175/BAMS-D-20-0148.1.
Li, M. Q., E. Virguez, R. Shan, J. L. Tian, S. Gao, and D. Patiño-Echeverri, 2022: High-resolution data shows China’s wind and solar energy resources are enough to support a 2050 decarbonized electricity system. Applied Energy, 306, 117996, https://doi.org/10.1016/j.apenergy.2021.117996.
Li, T., A. Li, and X. P. Guo, 2020: The sustainable development-oriented development and utilization of renewable energy industry-A comprehensive analysis of MCDM methods. Energy, 212, 118694, https://doi.org/10.1016/j.energy.2020.118694.
Liu, M. Q., X. A. Xia, D. S. Fu, and J. Q. Zhang, 2021: Development and validation of machine-learning clear-sky detection method using 1-min irradiance data and sky imagers at a polluted suburban site, Xianghe. Remote Sensing, 13, 3763, https://doi.org/10.3390/rs13183763.
Mouhamet, D., A. Tommy, A. Primerose, and L. Laurent, 2018: Improving the Heliosat-2 method for surface solar irradiation estimation under cloudy sky areas. Solar Energy, 169, 565−576, https://doi.org/10.1016/j.solener.2018.05.032.
Nonnenmacher, L., and C. F. M. Coimbra, 2014: Streamline-based method for intra-day solar forecasting through remote sensing. Solar Energy, 108, 447−459, https://doi.org/10.1016/j.solener.2014.07.026.
Peng, Z., and Coauthors, 2020: Estimation of shortwave solar radiation using the artificial neural network from Himawari-8 satellite imagery over China. Journal of Quantitative Spectroscopy and Radiative Transfer, 240, 106672, https://doi.org/10.1016/j.jqsrt.2019.106672.
Pfeifroth, U., S. Kothe, J. Trentmann, R. Hollmann, P. Fuchs, J. Kaiser, and M. Werscheck, 2019: Surface Radiation Data Set - Heliosat (SARAH) - Edition 2.1. Available from
Prăvălie, R., C. Patriche, and G. Bandoc, 2019: Spatial assessment of solar energy potential at global scale. A geographical approach. Journal of Cleaner Production, 209, 692−721, https://doi.org/10.1016/j.jclepro.2018.10.239.
Randles, C. A., and Coauthors, 2017: The MERRA-2 aerosol reanalysis, 1980 Onward. Part I: System description and data assimilation evaluation. J. Climate, 30, 6823−6850, https://doi.org/10.1175/JCLI-D-16-0609.1.
Razagui, A., K. Abdeladim, K. Bouchouicha, N. Bachari, S. Semaoui, and A. Hadj Arab, 2021: A new approach to forecast solar irradiances using WRF and libRadtran models, validated with MERRA-2 reanalysis data and pyranometer measures. Solar Energy, 221, 148−161, https://doi.org/10.1016/j.solener.2021.04.024.
Rigollier, C., M. Lefèvre, and L. Wald, 2004: The method Heliosat-2 for deriving shortwave solar radiation from satellite images. Solar Energy, 77, 159−169, https://doi.org/10.1016/j.solener.2004.04.017.
Senatla, M., and R. C. Bansal, 2018: Review of planning methodologies used for determination of optimal generation capacity mix: The cases of high shares of PV and wind. IET Renewable Power Generation, 12, 1222−1233, https://doi.org/10.1049/iet-rpg.2017.0380.
Shi, H. R., and Coauthors, 2021: Surface brightening in eastern and central China since the implementation of the clean air action in 2013: Causes and implications. Geophys. Res. Lett., 48, e2020GL091105, https://doi.org/10.1029/2020GL091105.
Sun, X. X., J. M. Bright, C. A. Gueymard, X. Y. Bai, B. Acord, and P. Wang, 2021: Worldwide performance assessment of 95 direct and diffuse clear-sky irradiance models using principal component analysis. Renewable and Sustainable Energy Reviews, 135, 110087, https://doi.org/10.1016/j.rser.2020.110087.
Wang, F., Z. Zhen, C. Liu, Z. Q. Mi, B.-M. Hodge, M. Shafie-Khah, and J. P. S. Catalão, 2018: Image phase shift invariance based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting. Energy Conversion and Management, 157, 123−135, https://doi.org/10.1016/j.enconman.2017.11.080.
Wang, P., R. van Westrhenen, J. F. Meirink, S. van der Veen, and W. Knap, 2019: Surface solar radiation forecasts by advecting cloud physical properties derived from Meteosat Second Generation observations. Solar Energy, 177, 47−58, https://doi.org/10.1016/j.solener.2018.10.073.
Wild, M., D. Folini, F. Henschel, N. Fischer, and B. Müller, 2015: Projections of long-term changes in solar radiation based on CMIP5 climate models and their influence on energy yields of photovoltaic systems. Solar Energy, 116, 12−24, https://doi.org/10.1016/j.solener.2015.03.039.
Xian, D., P. Zhang, L. Gao, R. J. Sun, H. Z. Zhang, and X. Jia, 2021: Fengyun meteorological satellite products for earth system science applications. Adv. Atmos. Sci., 38, 1267−1284, https://doi.org/10.1007/s00376-021-0425-3.
Yang, L. W., X. Q. Gao, Z. C. Li, D. Y. Jia, and J. X. Jiang, 2019: Nowcasting of surface solar irradiance using Fengyun-4 satellite observations over China. Remote Sensing, 11, 1984, https://doi.org/10.3390/rs11171984.
Yang, L. W., X. Q. Gao, J. J. Hua, P. P. Wu, Z. C. Li, and D. Y. Jia, 2020: Very short-term surface solar irradiance forecasting based on Fengyun-4 geostationary satellite. Sensors, 20, 2606, https://doi.org/10.3390/s20092606.
Zhang, J. Q., X. A. Xia, H. R. Shi, X. M. Zong, and J. Li, 2020: Radiation and aerosol measurements over the Tibetan Plateau during the Asian summer monsoon period. Atmospheric Pollution Research, 11, 1543−1551, https://doi.org/10.1016/j.apr.2020.06.017.
Zhu, T. T., H. Zhou, H. K. Wei, X. Zhao, K. J. Zhang, and J. X. Zhang, 2019: Inter-hour direct normal irradiance forecast with multiple data types and time-series. Journal of Modern Power Systems and Clean Energy, 7, 1319−1327, https://doi.org/10.1007/s40565-019-0551-4.
Zou, L., L. C. Wang, J. R. Li, Y. B. Lu, E. Gong, and Y. Niu, 2019: Global surface solar radiation and photovoltaic power from Coupled Model Intercomparison Project Phase 5 climate models. Journal of Cleaner Production, 224, 304−324, https://doi.org/10.1016/j.jclepro.2019.03.268.