Multi-stage Optimization Forecast of Short-term Power Load Based on VMD and PSO-SVR

doi:10.11930/j.issn.1004-9649.202109157

Abstract

Abstract: To reduce the non-linearity of the short-term load sequence and improve the prediction accuracy, a short-term load forecasting model based on multi-stage optimization variational mode decomposition (VMD) and particle swarm optimization optimize support vector regression (PSO-SVR) is proposed. In the first stage, VMD optimization and pre-processing of the original load sequence are used to decompose and obtain multiple relatively stable modal components. In the second stage, phase space reconstruction is used to optimize and reorganize each sequence component, and establish support vector regression（SVR）prediction model for each component. In the third stage, the particle swarm optimization（PSO）algorithm is applied to optimize the internal parameters of the SVR model to facilitate better training and forecasting. Finally, the predicted values of all sequences are accumulated to realize the short-term power load forecast. The results show that the proposed method can achieve higher prediction accuracy.

Key words: short-term power load forecasting, variational mode decomposition, phase space reconstruction, particle swarm optimization

LI Wenwu, SHI Qiang, LI Dan, HU Qunyong, TANG Yun, MEI Jinchao. Multi-stage Optimization Forecast of Short-term Power Load Based on VMD and PSO-SVR[J]. Electric Power, 2022, 55(8): 171-177.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202109157

https://www.electricpower.com.cn/EN/Y2022/V55/I8/171

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