A Concise Overview on Solar Resource Assessment and Forecasting

被引:1
作者
Dazhi YANG [1 ]
Wenting WANG [1 ]
Xiangao XIA [2 ]
机构
[1] School of Electrical Engineering and Automation, Harbin Institute of Technology
[2] Key Laboratory for Middle Atmosphere and Global Environment Observation (LAGEO),Institute of Atmospheric Physics, Chinese Academy of Sciences
关键词
D O I
暂无
中图分类号
TK51 [太阳能技术];
学科分类号
080703 ;
摘要
China's recently announced directive on tackling climate change, namely, to reach carbon peak by 2030 and to achieve carbon neutrality by 2060, has led to an unprecedented nationwide response among the academia and industry. Under such a directive, a rapid increase in the grid penetration rate of solar in the near future can be fully anticipated. Although solar radiation is an atmospheric process, its utilization, as to produce electricity, has hitherto been handled by engineers. In that,it is thought important to bridge the two fields, atmospheric sciences and solar engineering, for the common good of carbon neutrality. In this überreview, all major aspects pertaining to solar resource assessment and forecasting are discussed in brief. Given the size of the topic at hand, instead of presenting technical details, which would be overly lengthy and repetitive, the overarching goal of this review is to comprehensively compile a catalog of some recent, and some not so recent, review papers, so that the interested readers can explore the details on their own.
引用
收藏
页码:1239 / 1251
页数:13
相关论文
共 44 条
[21]  
Estimating surface solar irradiance from satellites: Past, present, and future perspectives[J] . Huang Guanghui,Li Zhanqing,Li Xin,Liang Shunlin,Yang Kun,Wang Dongdong,Zhang Yi.Remote Sensing of Environment . 2018 (C)
[22]  
Review on probabilistic forecasting of photovoltaic power production and electricity consumption[J] . D.W. van der Meer,J. Widén,J. Munkhammar.Renewable and Sustainable Energy Reviews . 2018
[23]  
Machine learning methods for solar radiation forecasting: A review[J] . Cyril Voyant,Gilles Notton,Soteris Kalogirou,Marie-Laure Nivet,Christophe Paoli,Fabrice Motte,Alexis Fouilloy.Renewable Energy . 2017
[24]  
Combining dynamical and statistical ensembles[J] . M.S. Roulston,L.A. Smith.Tellus A: Dynamic Meteorology and Oceanography . 2016 (1)
[25]  
Intercomparison of 51 radiometers for determining global horizontal irradiance and direct normal irradiance measurements[J] . Aron Habte,Manajit Sengupta,Afshin Andreas,Stephen Wilcox,Thomas Stoffel.Solar Energy . 2016
[26]  
WRF-SOLAR: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction[J] . Pedro A Jimenez,Joshua P Hacker,Jimy Dudhia,Sue Ellen Haupt,Jose A Ruiz-Arias,Chris A Gueymard,Gregory Thompson,Trude Eidhammer,Aijun Deng.Bulletin of the American Meteorological Society . 2016 (7)
[27]  
Solar radiation on inclined surfaces: Corrections and benchmarks[J] . Dazhi Yang.Solar Energy . 2016
[28]  
Extensive worldwide validation and climate sensitivity analysis of direct irradiance predictions from 1-min global irradiance[J] . Christian A. Gueymard,Jose A. Ruiz-Arias.Solar Energy . 2015
[29]   The quiet revolution of numerical weather prediction [J].
Bauer, Peter ;
Thorpe, Alan ;
Brunet, Gilbert .
NATURE, 2015, 525 (7567) :47-55
[30]  
Minute resolution estimates of the diffuse fraction of global irradiance for southeastern Australia[J] . N.A. Engerer.Solar Energy . 2015