基于CVMD-TCN-BiLSTM的短期电力负荷预测  

Short-term Electricity Load Forecasting Based on CVMD-TCN-BiLSTM

作　　者：杨汪洋 魏云冰 罗程浩 YANG Wangyang;WEI Yunbing;LUO Chenghao(School of Electrical and Electronic Engineering,Shanghai University of Engineering and Technology,Shanghai 201620)

机构地区：[1]上海工程技术大学电子电气工程学院,上海201620

出　　处：《电气工程学报》2024年第2期163-172,共10页Journal of Electrical Engineering

基　　金：国家自然科学基金资助项目(62173222)。

摘　　要：短期负荷数据往往掺杂着不同类型的噪声且波动性大,传统的分解序列方法在提取序列特征时并未考虑到高频噪声的影响。针对上述精准预测问题,介绍一种分解去噪、重构分解的CVMD-TCN-BiLSTM组合预测方法。采用互补集合经验模态分解(Complementary ensemble empirical mode decomposition,CEEMD)将原始电力负荷数据分解成一组比较稳定的子序列,联合小波阈值法将含有噪声的高频分量去噪,保留含有信号的低频分量进行累加重构。然后利用变分模态分解(Variational mode decomposition,VMD)对去噪后的数据进行二次信号特征提取,得到一组平稳性强且含不同频率的分量。最后,利用时序卷积网络-双向长短时记忆神经网络对各分量进行了预测,并将预测结果进行迭代,获得完整的预测结果。通过对澳大利亚某地的负荷数据作为实例分析,与传统的负荷预测算法相比,验证了所提模型的有效性。Short-term load data is often mixed with different types of noise and high volatility.Traditional sequence decomposition methods do not consider the influence of high-frequency noise when extracting sequence features.To address the above problems of accurate forecasting,a combined CVMD-TCN-BiLSTM forecasting method with decomposition denoising and reconstructive decomposition is introduced.The original electric load data are decomposed into a set of relatively smooth subsequences using the complementary ensemble empirical mode decomposition(CEEMD),and the high frequencies component with noise is denoised jointly with the wavelet thresholding method,and the low frequency components with signal are retained for cumulative decomposition.Then,the denoised data are subjected to secondary signal feature extraction using variational modal decomposition(VMD)to obtain a set of components with strong smoothness and containing different frequencies.Finally,the prediction of each component is performed using a temporal convolutional network-bi-directional long-and short-term memory neural network(TCN-BiLSTM),and the prediction results are iterated to obtain the complete prediction results.By analyzing the load data of a place in Australia as an example,compared with the traditional load forecasting algorithm,the effectiveness of the proposed model is verified.

关 键 词：互补集合经验模态分解 小波阈值 变分模态分解 分解去噪 时序卷积网络 双向长短时记忆神经网络 负荷预测 

分 类 号：TM74[电气工程—电力系统及自动化]