In order to improve the accuracy of short-term load forecasting of power system, the paper proposes a power load forecasting model RVR based on particle swarm optimization (PSO), and compares it with the support vector regression model. Aiming at the randomness of the parameters of the correlation vector regression, that is, the penalty function and the kernel function in the initialization, the PSO algorithm is used to optimize the parameters of the correlation vector regression, which can achieve better prediction results. The classical particle swarm optimization algorithm is a global optimization algorithm that can quickly find the optimal parameters in the correlation vector regression. The RVR model based on the particle swarm optimization is applied to short-term load forecasting. The simulation results show that the convergence rate of the optimized model of particle swarm optimization is more accurate than that of the traditional prediction models of SVR and RVR, and the predicting accuracy of the PSO – RVR model is higher than that of the PSO - SVR, which verifies the feasibility of the correlation vector regression method based on particle swarm optimization algorithm in the short-term load forecasting, which has practical value to some degree.