Prediction of solar irradiance based on Python - YAN, LITING; YU, AO; ZHANG, GE; ZHANG, JINYE (MAUSAM, 2023)
The rapid development of modern industrial society has relied heavily on cheap and abundant fossil fuel energy. However, to achieve sustainable development, there is an increasing focus on developing new energy sources such as photovoltaics (PV) and wind energy. In the context of using solar irradiance to generate electricity, predicting the solarpower in advance is crucial for efficient utilization. This paper utilizes the pvlib-python model to predict three types of irradiance in clear sky conditions: POA_DNI, POA_GHI, and POA_DHI. Furthermore, we incorporate aerosol data from pvlib to improve the prediction accuracy.Three sites from BSRN are selected and the predicted data are compared with the observed data to evaluate the model’s prediction effectiveness. The result reveals that the model performs best for POA_GHI and the actual cloud cover distribution has a significant impact on the prediction accuracy.