基于模态分解和误差修正的短期电力负荷预测  

Short-term electric load forecasting based on mode decompositionand error correction

作　　者：鄢化彪[1] 李东丽 黄绿娥[1] 张航菘 姚龙龙 Yan Huabiao;Li Dongli;Huang Lyu′e;Zhang Hangsong;Yao Longlong(School of Science,Jiangxi University of Science and Technology,Ganzhou 341000,China)

机构地区：[1]江西理工大学理学院,赣州市341000

出　　处：《电子测量技术》2025年第5期92-101,共10页Electronic Measurement Technology

基　　金：江西省自然科学基金(20224BAB202036);江西省教育厅科学技术重点研究项目(GJJ2200805)资助。

摘　　要：针对电力负荷非线性、高波动性和强随机性等特性导致无法充分提取时序特征引起预测误差较大的问题,提出了基于改进的自适应白噪声完全集合经验模态分解和误差修正的双向时间卷积网络-双向长短期记忆网络短期电力负荷预测方法。先由最大信息系数筛选出与负荷高度相关的特征集,以削弱特征冗余;通过改进的自适应白噪声完全集合经验模态分解将高波动性的负荷分解为频率各异的本征模态分量和残差,以降低非平稳性;引入样本熵将复杂度相近的分量重构成新子序列,以降低计算量;然后,结合并行双向时间卷积网络提取不同尺度的特征,利用双向长短期记忆网络对负荷序列初步预测,使用麻雀优化算法对神经网络超参数调优;最后,误差序列通过误差修正模块对初始预测值进行修正。经实验验证,与其他预测模型相比,RMSE最多降低51.42%,最少降低34.26%,验证了模型的准确性和有效性。In order to solve the problem that the time series features cannot be fully extracted due to the characteristics of power load,such as nonlinearity,high volatility and strong randomness,a short-term power load prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise and a deep error correction multi-scale bidirectional temporal convolution network-bidirectional long short-term memory network is proposed.First,the maximum information coefficient is used to select a highly relevant feature set to reduce feature redundancy.Then,improved complete ensemble empirical mode decomposition with adaptive noise is employed to decompose the highly fluctuating load into intrinsic mode functions with different frequencies and residual,while sample entropy is introduced to reconstruct new subsequences with similar complexities to reduce computational load.Next,using the optimal feature set,each subsequence is forecasted with a multi-scale bidirectional temporal convolution network-bidirectional long short-term memory network model optimized by the sparrow search algorithm to obtain initial load sequence predictions.Finally,the error sequence is corrected using the error correction module to refine the initial predictions.Experimental verification shows that compared with other prediction models,RMSE is reduced by 51.42%at most and 34.26%at least,which verifies the accuracy and effectiveness of the model.

关 键 词：电力负荷 短期预测 自适应经验模态分解 样本熵 双向时间卷积网络 双向长短期记忆 麻雀搜索算法 

分 类 号：TM715[电气工程—电力系统及自动化] TN0[电子电信—物理电子学]