A Comparative Study of Photovoltaic Power Prediction Models Based on BP and SVR

The power output of photovoltaic (PV) generation is influenced by various environmental factors, exhibiting non-linearity and uncertainty. This paper constructs photovoltaic power prediction models based on BP neural network and support vector regression (SVR) algorithms, and performs a comparative analysis of model performance using metrics such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>). The results show that the BP neural network is suitable for handling complex nonlinear large-scale data, while SVR demonstrates higher accuracy and generalization ability under small sample conditions. This study provides a reference for the selection and optimization of photovoltaic power prediction models.