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The rapid development of photovoltaic plays an important role in achieving the carbon-neutral goal. How to improve the conversion efficiency and power generation of solar photovoltaic has always been a focus issue. However, more attention is paid to the impact of photovoltaic panel working temperature on the performance of photovoltaic power generation, and how air temperature affects photovoltaic power generation has been ignored. This paper compared and analyzed the impact of the difference in air temperature between lake and land on the revenue of photovoltaic power generation, and established the functional equation between photovoltaic power generation, air temperature and solar radiation, and revealed the relationship between air temperature, solar radiation, and photovoltaic power generation. [Display omitted] [ABSTRACT FROM AUTHOR]

The global warming effect has been accelerating rapidly and poses a threat to human survival and health. The top priority to solve this problem is to provide reliable renewable energy. To achieve this goal, it is important to provide fast and accurate solar radiation predictions based on limited observation data. In this study, a fast and accurate solar radiation nowcasting method is proposed by combining FY-4A satellite data and the McClear clear sky model under the condition of only radiation observation. The results show that the random forest (RF) performed better than the support vector regression (SVR) model and the reference model (Clim-Pers), with the smallest normalized root mean square error (nRMSE) values (between 13.90% and 33.80%), smallest normalized mean absolute error (nMAE) values (between 7.50% and 24.77%), smallest normalized mean bias error (nMBE) values (between −1.17% and 0.7%) and highest R2 values (between 0.76 and 0.95) under different time horizons. In addition, it can be summarized that remote sensing data can significantly improve the radiation forecasting performance and can effectively guarantee the stability of radiation predictions when the time horizon exceeds 60 min. Furthermore, to obtain the optimal operation efficiency, the prediction results were interpreted by introducing the latest SHapley Additive exPlanation (SHAP) method. From the interpretation results, we selected the three key channels of an FY-4A and then made the model lightweight. Compared with the original input model, the new one predicted the results more rapidly. For instance, the lightweight parameter input model needed only 0.3084 s (compared to 0.5591 s for full parameter input) per single data point on average for the 10 min global solar radiation forecast in Yuzhong. Meanwhile, the prediction effect also remained stable and reliable. Overall, the new method showed its advantages in radiation prediction under the condition that only solar radiation observations were available. This is very important for radiation prediction in cities with scarce meteorological observation, and it can provide a reference for the location planning of photovoltaic power stations. [ABSTRACT FROM AUTHOR]

Using data observed at a photovoltaic (PV) power plant at the edge of the Gurbantünggüt Desert and at an undeveloped site in the Gobi desert in the summers of 2019 and 2020, we compared and analyzed the variations of radiation and surface albedo in various wavelength bands. Components of the solar radiation received by the surface of the arid regions of China were ranked infrared (IR) > visible (VIS) > ultraviolet (UV). The observed surface albedo of the PV site was significantly lower than that at the undeveloped desert site. The mean surface albedo values for shortwave broadband (SW), IR, VIS, and UV at the PV site were 0.14, 0.15, 0.13, and 0.08, respectively, and the corresponding values for the undeveloped site were 0.22, 0.23, 0.23, and 0.10, respectively. We also developed a parametric formula for the surface albedo at the PV plant given data observed on typical sunny days. We then validated the parametric formula using observations taken in the summers of 2019 and 2020. The results showed that the parametric formula could precisely simulate variations of the surface albedo and reflected radiation of the PV plant. [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]