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Solar energy plays an essential role in achieving carbon goals and mitigating climate change. Therefore, solar power plants are rapidly developing in the renewable energy sector. However, many reports of solar power plants are on land, and extremely limited observational research has been conducted on the impacts of fishery complementary photovoltaic power plants (FPVs) on near-surface meteorology and surface energy. This study selected two adjacent eddy covariance observational towers at the fishery complementary photovoltaic power plant (FPV) in Yangzhong, Jiangsu Province, China, to explore this topic. The results indicated that the percent frequency of east wind (<4 m s−1) at 2 m decreased by 25.3% in the FPV site compared with the reference site. The FPV array has not an obvious heating effect on the ambient environment. The average air temperature difference in the two sites at 2 m and 10 m was 0.3 K and 0.1 K, respectively, during the heating period. The net radiation increased by 47.8 W m−2 in the two sites. The sensible heat increased by 7.9 W m−2 due to the heating effect. The latent heat in the two sites is contrary to the sensible heat (−13.0 W m−2). The difference in the water storage heat is 32.18 W m−2, which implies that the water absorbs higher heat in the FPV site than in the reference site. This work illustrated the importance of observational experiments to animate process-based understanding combined with FPV systems and provides a scientific basis for establishing FPV energy balance models based on observations that may be used to reveal the impact of utility-scale FPV deployment on climatic effects. [ABSTRACT FROM AUTHOR]

Due to the rapid development of solar energy and photovoltaic industries in China, it is crucial to provide the reliable and accurate solar radiation predictions. In this work, three commonly used machine learning models for predicting global and diffuse solar radiation were assessed in eight Chinese cities, representing different geoclimatic and pollutant conditions. According to the results; regarding the nRMSE, nMAE, nMBE and R values, coastal locations (such as Shanghai, Guangzhou, etc.) obtained higher values than inland locations (such as Lanzhou and Wuhan). Moreover, the SVM (support vector machine) highly outperformed the other models in all locations, regardless of whether the study area was arid, semiarid, semihumid or humid, followed by GLMNET (generalized linear modeling) and RF (random forest). In addition, when assessing the SVM in different locations under different climatic and pollution conditions, it was indicated that the accuracy of solar radiation prediction was closely related to the weather and pollution condition levels. In general, the global solar radiation prediction error was in line with the weather condition levels. The prediction error increased as the weather level increased. However, the relationship between the pollution condition levels and the global solar radiation prediction showed a non-linear relationship. Moreover, for the prediction results of diffuse solar radiation, its variation law with different weather and pollution condition levels was almost different from that of global solar radiation. The maximum high error occurrence probability of global solar radiation and diffuse solar radiation appeared at pollution levels 5 and 1, respectively. Overall, the SVM model demonstrated its reliability in radiation prediction under slight pollution and stable weather conditions. This is crucial in locations with scarce meteorological data and can be used to optimize the selection of geographical locations for photovoltaic power station construction. [ABSTRACT FROM AUTHOR]