Short-term power load forecasting based on the PSO-RVR model

Yan Zhang; Genji Yuan; Hanran Ji; Hui Fan; Jinjiang Li

doi:10.1117/12.2501924

26 July 2018 Short-term power load forecasting based on the PSO-RVR model

Yan Zhang, Genji Yuan, Hanran Ji, Hui Fan, Jinjiang Li

Proceedings Volume 10828, Third International Workshop on Pattern Recognition; 108281J (2018) https://doi.org/10.1117/12.2501924
Event: Third International Workshop on Pattern Recognition, 2018, Jinan, China

Abstract

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.

Citation Download Citation

Yan Zhang, Genji Yuan, Hanran Ji, Hui Fan, and Jinjiang Li "Short-term power load forecasting based on the PSO-RVR model", Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108281J (26 July 2018); https://doi.org/10.1117/12.2501924

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