Research on the Method of Station Load Prediction Based on SVR Optimized by GS-PSO

Power load forecasting in the station area of power grid can ensure the reliability of the power distribution network in the station area, and it is a crucial means to ensure the correctness of managers' decisions. Therefore, aiming at the problem of low accuracy of power load forecasting, this paper adopts the support vector regression model optimized by particle swarm and grid search to predict the load. First, we use the k-nearest neighbor method to fill in missing values and deal with outliers. Then, the wavelet transform is used to remove the noise in the data and improve the data quality. Then, we use the support vector regression algorithm to train the prediction model. To improve the prediction accuracy of the model, we use the particle swarm optimization algorithm combined with the grid search algorithm to find the optimal parameters of the SVR. Experiment shows that our algorithm has better prediction accuracy than other algorithms.