[1]骆东松,魏義民,张杰锋.基于经验模态分解和优化 BiLSTM 的短期负荷预测[J].机械与电子,2024,42(09):11-17.
　LUO Dongsong,WEI Yimin,ZHANG Jiefeng.Short-term Load Forecasting Based on Empirical Modal Decomposition and Optimized BiLSTM[J].Machinery & Electronics,2024,42(09):11-17.
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基于经验模态分解和优化 BiLSTM 的短期负荷预测()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
42
期数:
2024年09期
页码:
11-17
栏目:
研究与设计
出版日期:
2024-09-27

文章信息/Info

Title:
Short-term Load Forecasting Based on Empirical Modal Decomposition and Optimized BiLSTM
文章编号:
1001-2257 ( 2024 ) 09-0011-07
作者:
骆东松; 魏義民; 张杰锋
兰州理工大学电气工程与信息工程学院,甘肃 兰州 730050
Author(s):
LUO Dongsong ;  WEI Yimin ;  ZHANG Jiefeng
( College of Electrical and Information Engineering , Lanzhou University of Science and Technology , Lanzhou 730050 , China )
关键词:
电力系统; 负荷预测; 经验模态分解; 麻雀搜索算法; 双向长短时记忆神经网络
Keywords:
power system ;  load forecasting ;  empirical mode decomposition ;  sparrow search algorithm ;  bidirectional long short-term memory neural network
分类号:
TP183 ; TM715
文献标志码:
A
摘要:
针对电力负荷数据的非线性和不稳定性问题,提出了一种基于经验模态分解 改进麻雀搜索算法-双向长短期记忆神经网络相结合的 EMD-ISSA-BiLSTM 预测模型。首先采用 EMD 处理非线性负荷数据,将原始负荷数据分解为多个不同尺度的本征模态函数( IMF ),引入反向学习策略和 Levy 飞行策略分别改进麻雀搜索算法( SSA )的收敛速度慢和容易陷入局部最优问题,利用改进麻雀搜索算法( ISSA )对 BiLSTM 神经网络进行参数寻优。然后再利用优化后的 BiLSTM 模型对每个分量进行预测,并将各预测结果叠加组合,得到整个负荷序列的预测结果。最后通过实际算例分析,证明该方法相对于传统的预测方法具有更好的预测精度和稳定性,可作为一种有效的短期负荷预测方法。
Abstract:
Aiming at the problem of nonlinearity and instability of load data , an EMD-ISSA-BiLSTM prediction model based on the combination of empirical modal decomposition improved sparrow search algorithm bidirectional long and short term memory neural network is proposed.Firstly , EMD is used to process the nonlinear load data , and the original load data are decomposed into several different scales of intrinsic modal functions ( IMFs ) and residuals ( Res ), and the inverse learning strategy and the Levy flight strategy are introduced to improve the convergence speed and local optimization problem of the Sparrow Search Algorithm ( SSA ), respectively , and the Improved Sparrow Search Algorithm ( ISSA ) is utilized to perform BiLSTM neural network parameter search optimization.Then the optimized BiLSTM model is used to predict each component , and the prediction results of each prediction are superimposed and combined to obtain the prediction results of the whole load sequence.Finally , through the analysis of actual cases , it is proved that this method has better prediction accuracy and stability than the traditional prediction methods , and can be used as an effective short-term load prediction method.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2023-11-14
作者简介:骆东松 ( 1970- ),男,甘肃天水人,教授,研究方向为嵌入式系统、计算机控制系统的研究与开发等;魏義民 ( 1998- ),男,甘肃泾川人,硕士研究生,研究方向为计算机控制系统的研究与开发;张杰锋 ( 2000- ),男,甘肃会宁人,硕士研究生,研究方向为计算机控制系统的研究与开发。
更新日期/Last Update: 2024-09-24