针对传统奇异值阈值(Singular Value Thresholding,SVT)数据恢复算法在对电力负荷数据恢复中忽视数据先验信息以及大规模数据计算效率低等问题,提出一种基于相空间重构与自适应变步长的改进SVT的数据恢复算法.为解决传统SVT容易忽视数据先验信息的问题,引入相空间重构算法将原始缺失数据映射到高维空间,利用数据间的关联性和结构特征,为后续数据恢复算法提供先验知识;结合对数与Sigmoid函数构建变步长基础函数,并利用等比项提高前期步长,构建自适应变步长SVT算法,克服传统SVT在大规模数据情况下计算效率低的问题.结合多项公用电力负荷数据集及多种常用电力负荷数据恢复算法进行对比实验分析,结果表明,改进SVT算法可获得更好的数据恢复效果,收敛速度、精度以及稳定性得到提升,具有较强的工程实用性.
An Enhanced Algorithm for Power Load Data Recovery Based on Improved SVT
In response to the issues of neglecting prior information and low computational efficiency of the traditional Singular Value Thresholding(SVT)algorithm in power load data recovery,a novel improved SVT algorithm based on phase space reconstruction and adaptive variable step length is proposed.To address the problem of neglecting prior information in traditional SVT,a phase space reconstruction algorithm is introduced to map the original missing data into a high-dimensional space,leveraging data correlation and structural features as prior knowledge for subsequent data recovery algorithms.By combining logarithmic and sigmoid functions to construct the variable step length base function,and utilizing geometric progression to enhance the initial step length,an adaptive variable step length SVT algorithm is built to overcome the low computational efficiency issue of traditional SVT in large-scale data scenarios.Comparative experimental analysis is conducted using multiple publicly available power load datasets and various commonly used power load data recovery algorithms.The results demonstrate that the improved SVT algorithm achieves better data recovery performance,with enhanced convergence speed,accuracy,and stability,showcasing strong engineering practicality.
electricity load datadata processingsingular value thresholdingphase space methodsadaptive variable step size
万方数据
维普
