Domestic and abroad research status and prospects of solar energy resource evaluation methods

doi:10.12006/j.issn.1673-1719.2022.096

Abstract

Abstract:

Solar energy is considered as the most promising renewable energy source with extensive application prospects. A reliable and accurate evaluation of solar resources is a primary and essential step before developing and utilizing solar resources. However, in most regions, solar radiation observations are too finite to evaluate solar resources directly. The solar energy resource evalution by other meteorlogical records is available. This paper reviews fundamental theories, the technology roadmap and the advances of four main solar energy resource evaluation methods in domestic and abroad studies. We also investigate errors of each method and discuss their disadvantages. The future development trend of the four methods in China has been prospected. The empirical model and artificial intelligence model highly depend on the characteristics of data. The description of the impacts of the cloud on the solar radiation is inaccurate in the phyical model. The data assimilation and parameterization scheme are critical in the numerical weather forecasting models. Developing hybrid evaluation model with various methods is one of the future directions of solar energy resource assessment.

Key words: Solar energy resource assessment, Empirical model, Physical model, Numerical weather prediction model, Artificial intelligence, Root mean square error

WANG Ke, HUANG Jing. Domestic and abroad research status and prospects of solar energy resource evaluation methods[J]. Climate Change Research, 2023, 19(2): 160-172.

Figures/Tables 5

Table 1 Number of meteorological stations with hourly radiation records of each province in China

Fig. 1 Schematic diagram of solar resource evaluation by the physical model

Fig. 2 Schematic diagram of calculation of the radiative transfer scheme in the numerical weather prediction model

Fig. 3 Schematic diagram of solar resource evaluation by the artificial neural network

Table 2 Minimum, maximum, and changes of root mean square error for four different models MJ/(m2∙d)

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