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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]

Large-scale solar power plants are rapidly increasing in size and number across the world. However, the surface heat balance is altered when a photovoltaic (PV) power plant is operating. Modifications to the surface albedo through the deployment of photovoltaic arrays have the potential to change radiative forcing, surface temperatures and local weather patterns. In this work, the field observation data from a large solar farm and a region without PV array in Golmud are used to study the impact of large solar farms in desert areas on the local meteorology. The results show that the total daily values of upward shortwave radiation and net radiation in the two sites are significantly different. The mean daily albedo in the solar farm is 0.19, while it is 0.26 in the region without PV. The annual mean net radiation in the solar farm is evidently higher than that of the region without PV. The daily range of soil temperatures at a depth of 5–10 cm in the solar farm is lower than that in the region without a PV farm. The annual range of soil temperatures at a depth of 5–180 cm in the solar farm is higher than that in the region without PV. The soil temperatures at different depths in winter in the solar farm are clearly lower than those in the region without PV. The daily mean of soil temperatures at a depth of 5–80 cm from October 2012 to March 2013 is clearly lower than that in the region without a PV array. The 2-m daytime air temperature in the two sites is essentially the same during winter, while during the other seasons, the daytime air temperature in the PV farm is higher than that in the region without PV, with the maximum difference appearing during the summer. The nighttime air temperatures at a height of 2 m during the four seasons in the solar farm are higher than those in the region without PV. The monthly average 2-m air temperature in the solar farm is higher than that in the region without PV. [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]

• Microclimate and soil characteristics under Gobi PV arrays were first observed. • South-facing PV panels reduced wind speed with the prevailing northerly wind below. • PV panels had buffer effects on microclimate and soil thermal regime change. • Annual net radiation reduction was up to 92.68% since the shading of PV. • The significant increase in relative humidity during winter was caused by snow cover. The high demand for low-carbon energy sources to mitigate climate change has prompted a rapid increase in ground-mounted solar parks. The implementation of photovoltaic (PV) significantly impacted the local climate and ecosystem, which are both poorly understood. To investigate the effects of a typical solar park on the Gobi ecological system, local microclimate and soil thermal regimes were measured year-round under and between PV arrays, at an applied solar park sited in Xinjiang, China. Our results demonstrated their seasonal and diurnal changes. Under solar PV arrays, the mean annual net radiation and wind speed decreased by 92.68 % and 50.53 % respectively. In contrast, PV panels caused an increase of the rear sides air by 10.12 % with 0.87 °C. South-facing PV panels reduced wind speed with the prevailing northerly wind below. In addition, the relative humidity rapidly decreased when snow covered the ground, but slightly increased from April to September. We found the soil under PV panels was cooler and tended to be a sink of energy during spring and summer whereas was more often a source during autumn and winter compared with the soil between PV panels. Observed data developed the understanding of the energy processes of solar parks in Gobi ecosystems and provided evidence to support the sustainable management of the solar park. [ABSTRACT FROM AUTHOR]