期刊论文

Multi-factor Short-term Load Prediction Model Based on PCA-DBILSTM

中文

电力系统及其自动化学报

1003-8930

2020

32

12

32-39

10.19635/j.cnki.csu-epsa.000501

国家自然科学基金资助项目(51877012) 湖南省教育厅重点资助项目(18A121) 湖南省研究生科研创新资助项目(CX2018B557)。

本校为第一机构

针对传统神经网络在短期负荷预测中预测精度不高、预测时间较长的问题,提出了一种基于主成分分析法和深度双向长短期记忆神经网络的短期负荷预测模型。该模型运用主成分分析法对原始多维输入变量组成的时间序列进行主成分提取,实现原始负荷的降维;然后通过深度双向长短期记忆网络结合Adamax优化算法,对提取的主成分序列和负荷实际输出序列之间的非线性关系建立网络模型。以中国某地区的负荷数据作为实际算例,验证该方法预测精度达到了99.44%,并与传统预测模型进行对比,在保证预测精度的同时,大幅降低了预测时间。

Aimed at the problem that the traditional neural network has low prediction accuracy and long prediction time in short-term load prediction,a short-term load prediction model based on principal component analysis(PCA)and deep bi-directional long-short-term memory(DBILSTM)neural network is proposed.This model uses PCA to ex⁃tract the principal components of the time series that consists of the original multi-dimensional input variables,thus achieving the dimensionality reduction of the original load.Then,the DBILSTM network is combined with the...