[1]蒋建东,韩文轩,赵云飞,等.基于优化VMD二次分解的短期电力负荷预测[J].郑州大学学报(工学版),2026,47(01):124-130.[doi:10.13705/j.issn.1671-6833.2025.04.016]
　JIANG Jiandong,HAN Wenxuan,ZHAO Yunfei,et al.Short-term Power Load Forecasting Based on Optimized VMD Secondary Decomposition[J].Journal of Zhengzhou University (Engineering Science),2026,47(01):124-130.[doi:10.13705/j.issn.1671-6833.2025.04.016]
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基于优化VMD二次分解的短期电力负荷预测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年01期
页码:
124-130
栏目:
出版日期:
2026-01-06

文章信息/Info

Title:
Short-term Power Load Forecasting Based on Optimized VMD Secondary Decomposition
文章编号:
1671-6833(2026)01-0124-07
作者:
蒋建东1;  韩文轩1;  赵云飞1;  燕跃豪2;  鲍　薇2;  刘晓辉2
1.郑州大学 电气与信息工程学院,河南 郑州 450001;2.国网河南省电力公司郑州供电公司,河南 郑州 450006
Author(s):
JIANG Jiandong1;  HAN Wenxuan1;  ZHAO Yunfei1;  YAN Yuehao2;  BAO Wei2;  LIU Xiaohui2
1.School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2.Zhengzhou Power Supply Company of State Grid Henan Electric Power Company, Zhengzhou 450006, China
关键词:
二次分解;  负荷预测;  完全自适应噪声集合经验模态分解;  变分模态分解;  时间卷积网络
Keywords:
secondary decomposition;  load forecasting;  complete ensemble empirical mode decomposition with adaptive noise;  variational mode decomposition;  temporal convolutional network
分类号:
TM715TP183
DOI:
10.13705/j.issn.1671-6833.2025.04.016
文献标志码:
A
摘要:
针对台区配变负荷数据复杂度高、波动性强的特点,提出了一种基于二次分解和时间卷积网络的短期电力负荷预测模型。首先,使用最大互信息系数法对高维特征的负荷数据集进行特征提取;其次,采用完全自适应噪声集合经验模态分解和优化变分模态分解对配变负荷数据进行二次分解;再次,将两次分解得到的子序列输入时间卷积网络模型中进行预测;最后,将各子序列的预测结果叠加,得到最终的负荷预测结果。在郑州市某台区配变负荷数据上进行仿真分析,与传统时间卷积网络模型相比,所提模型MAE、MAPE和RMSE分别减少了64.29%,9.66百分点和59.00%。实验结果表明,所提组合预测模型具有更好的预测效果和更高的预测精度。
Abstract:
A short-term power load forecasting model based on secondary decomposition and temporal convolutional networks was proposed in response to the high complexity and strong fluctuation of transformer load data in the station area. Firstly, the maximum information coefficient method was used to extract features from the high-dimensional load dataset. Secondly, complete ensemble empirical mode decomposition with adaptive noise and optimized variational mode decomposition were employed to perform secondary decomposition on the transformer load data. Then, the sub-sequences obtained from the two decompositions were input into the temporal convolutional network model for prediction. Finally, the prediction results of each sub-sequence were superimposed to obtain the final load forecasting result. Simulation analysis was conducted on the load data of a distribution transformer in a certain district of Zhengzhou City. Compared with the traditional time convolutional network model, the proposed model reduced MAE, MAPE, and RMSE by 64.29%, 9.66 percentage points, and 59.00% respectively. The experimental results showed that the proposed combined forecasting model had better forecasting effects and higher prediction accuracy.

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更新日期/Last Update: 2026-01-17