Photovoltaic power prediction algorithm based on BP neural network

With the rapid development of the global economy, non-renewable resources have been developed in large quantities, and there is great potential for the utilization of renewable resources such as solar energy. The prediction of solar power is the key to ensure power supply and dispatch. In order to ensure the stability and accuracy of prediction and reduce the adverse effects of instability factors on power system operation, BP neural network is introduced to forecast photovoltaic power generation. In this research, the Pearson correlation coefficient method is employed to identify the primary factors influencing PV power generation, namely temperature, humidity, and light radiation intensity. A BP neural network model is then constructed to predict PV power output. Data normalization techniques are applied to expedite the training process and optimize the network’s performance. Subsequent data analysis and model validation demonstrate that this approach can provide accurate and efficient predictions of PV power generation.