Prediction of solar irradiance based on Python


  • LITING YAN Hubei University of Technology
  • AO YU Hubei University of Technology, School of Science, Wuhan, China, 430068
  • GE ZHANG Hubei University of Technology, School of Science, Wuhan, China, 430068



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.

Author Biography


Associate Professor, Department of Microelectronics, School of Science, Hubei University of Technology. Dr. Zhang graduated from the State Key Laboratory of Surveying, Mapping and Remote Sensing Information Engineering, Wuhan University. Main research: OPTICAL atmospheric environment remote sensing, LIDAR detection technology, solar radiation detection and solar energy generation prediction. 




How to Cite

L. YAN, A. YU, G. ZHANG, and J. ZHANG, “Prediction of solar irradiance based on Python”, MAUSAM, vol. 74, no. 4, pp. 1029–1042, Oct. 2023.



Research Papers