计算机工程2025，Vol.51Issue(2)：375-386,12.DOI:10.19678/j.issn.1000-3428.0069241
基于改进变分模态分解与深度学习的多因素电力负荷预测
Multi-factor Power Load Forecasting Based on Improved Variational Mode Decomposition and Deep Learning
摘要
Abstract
To address the problems of low accuracy and large noise of load data in traditional power load forecasting methods,this study proposes a multi-factor power load forecasting method based on improved Variational Modal Decomposition(VMD),Convolutional Neural Network(CNN),and deformed length short-term memory network(Mogrifier LSTM).First,the Sparrow Search Algorithm(SSA)is used to optimize the VMD and obtain the decomposition subsequence with the best effect,which effectively reduces the influence of load data noise on the prediction accuracy.Second,the influence mechanism of each factor on load prediction is analyzed,the correlation between each influencing factor and load is derived using the Pearson's correlation coefficient,and redundant features are removed,which greatly reduces the probability of model inaccuracy.Finally,a CNN is employed to extract feature vectors.The decomposed load data and feature data such as temperature and humidity are fed into the CNN-Mogrifier LSTM deep network model.The feature data are analyzed in multiple dimensions in this model to improve the short-term load prediction accuracy.The results show that the multi-factor power load prediction model proposed in this study has good adaptability and prediction effects.Compared with the suboptimal VMD-CNN-Mogrifier LSTM model,the prediction accuracy of the proposed model on two real datasets is improved by 0.5 and 2.4 percentage points,respectively,which provides a feasible solution for short-term power load forecasting.关键词
负荷预测/麻雀搜索算法/变分模态分解/长短期记忆网络/相关分析Key words
load forecasting/Sparrow Search Algorithm(SSA)/variational mode decomposition/Long Short-Term Memory(LSTM)network/correlation analysis分类
信息技术与安全科学引用本文复制引用
赖小玲,贺嫚嫚,胡伟,张艺,杜璞良,刘蕊,宋晓彤,郑婷婷..基于改进变分模态分解与深度学习的多因素电力负荷预测[J].计算机工程,2025,51(2):375-386,12.基金项目
国家社科基金项目(19BGL003). （19BGL003）
