Photovoltaic Power Prediction Based on Improved Sparse Bayesian Regression

Abstract The photovoltaic grid connection can impact the power grid and affect its stability; therefore, making predictions about photovoltaic power is critically important for the grid scheduling department to properly plan power generation. The characteristics of photovoltaic power are analyzed, and the principle of sparse Bayesian regression is studied; thus, a photovoltaic power prediction model based on the sparse Bayesian regression algorithm is established. Traditional sparse Bayesian regression uses the maximum likelihood method to optimize hyper-parameters, which has some disadvantages, for example, the optimization effect excessively depends on initial values and iterations are difficult to determine. In this article, the artificial bee colony is used instead of the maximum likelihood method to optimize the hyper-parameters. An improved sparse Bayesian regression model based on artificial bee colony optimization is proposed that considers meteorological factors and historical power data. Finally, the state grid Scenery Storage Demonstration Project data are used to test the proposed prediction model. The simulation result shows that the improved sparse Bayesian regression model achieves good prediction effects.