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

doi:10.16182/j.issn1004731x.joss.201708025

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.

Key words: support vector machine, particle swarm optimization, second-order oscillation, repulsion factor, data analysis, short-term load forecasting

CLC Number:

TP391

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.

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