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新疆大学电气工程学院
Published:2020
移动端阅览
[1]牛安敏,张宏立,王聪.基于VMD-Leaky-ESN的电力系统短期负荷多步预测方法[J].新疆大学学报(自然科学版)(中英文),2020,37(04):562-569.
[1]牛安敏,张宏立,王聪.基于VMD-Leaky-ESN的电力系统短期负荷多步预测方法[J].新疆大学学报(自然科学版)(中英文),2020,37(04):562-569. DOI: 10.13568/j.cnki.651094.651316.2019.12.20.0002.
DOI:10.13568/j.cnki.651094.651316.2019.12.20.0002.
针对电力系统负荷序列非线性、非平稳特征
为对其进行准确短期预测
文章提出了基于VMD-Leaky-ESN的电力系统短期负荷多步预测方法.首先对原始负荷时间序列进行归一化处理
采用变分模态分解(Variational Mode Decomposition
VMD)方法对序列分解处理
得到不同频率尺度的子序列
并引入模态分量样本熵来降低任务量并改善预测效果;其次采用泄露积分型回声状态网络(Leaky Integrator Echo State Network
Leaky-ESN)结合多步预测
对组合后的模态分量序列进行短期电力负荷预测
最后通过5种短期负荷预测模型对比分析
证明该预测方法可有效避免预测过程中累计的迭代误差
具有更高的预测精度和泛化能力.
Aiming at the nonlinear and nonstationary characteristics of power system load series
a multi-step short-term load forecasting method based on VMD-leaky-ESN algorithm to accurately predict the short-term load with the above characteristics is proposed in this paper. Firstly
the original load time series is normalized
and the sequence is decomposed by the method of variable mode decomposition(VMD)
then the subsequences of different frequency scales are obtained
and the sample entropy of modal components is introduced to reduce the task amount and improve the prediction effect.Secondly
leaky integrator echo state network(Leaky-ESN) combined with multistep prediction is used to predict the short-term power load of the combined modal component sequence. Finally
through the comparative analysis of five short-term load forecasting models
it is proved that the prediction method can effectively avoid the accumulated iterative error in the prediction process
and has higher prediction accuracy and generalization ability.
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