Forecasting of Short-term Power Load of SecRPSO-SVM Based on Data-driven

Authors

Hairong Sun, 1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;;
Bixia Xie, 1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;;2. Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China;
Tian Yao, 1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;;2. Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China;
Zhuoqun Li, 1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;;

Abstract

Abstract: For the parameter selection of support vector machine in modeling, a particle swarm optimization algorithm based on second-order oscillation and repulsion factor was proposed to optimize the parameter of SVM. The algorithm employed the nonlinear decreasing weight to balance the global and local search ability. Second-order oscillation factor could maintain the population diversity. The repulsion factor was introduced to make the swarm even distribution in search space, which could avoid local optimum. For the complex characteristics of nonlinearity, time-varying and multifactorial of electric power load, a support vector machine forecasting model based on data was proposed, and the influence of weather, time and historical load on the forecast results was considered. Simulation results show that the proposed method can be used to build an effective and high precision short-term power load forecasting model.

Recommended Citation

Sun, Hairong; Xie, Bixia; Yao, Tian; and Li, Zhuoqun (2020) "Forecasting of Short-term Power Load of SecRPSO-SVM Based on Data-driven," Journal of System Simulation: Vol. 29: Iss. 8, Article 25.
DOI: 10.16182/j.issn1004731x.joss.201708025
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss8/25

First Page

1829

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201708025

Last Page

1836

CLC

TP391

Recommended Citation

Sun Hairong, Xie Bixia, Tian Yao, Li Zhuoqun. Forecasting of Short-term Power Load of SecRPSO-SVM Based on Data-driven[J]. Journal of System Simulation, 2017, 29(8): 1829-1836.

DOI

10.16182/j.issn1004731x.joss.201708025