Short-Term Photovoltaic Power Prediction Based on Bayesian Regularized BP Neural Networks

In view of the problem that the randomness and volatility of photovoltaic power generation lead to low prediction accuracy, this paper proposes a short-term prediction method for photovoltaic power generation power based on Bayesian regularization algorithm to optimize BP neural network, and analyzes the factors affecting photovoltaic power generation power prediction model for photovoltaic power generation was established, which was trained in neural networks by Bayesian regularization algorithm optimization, L-M optimization algorithm and traditional gradient drop method, and the prediction results of the three methods were compared with the study, and the experimental results showed that the method proposed in this paper was effective Improved photovoltaic power prediction accuracy, which can be used for short-term accurate prediction of photovoltaic power.