基于VMD与MOGOA-LSTM的短期负荷预测模型  

Short-term Load Forecasting Model Based on VMD and MOGOA-LSTM

作　　者：欧阳孟可 沈卫康[1] 成徽 石凯 OUYANG Meng-ke;SHEN Wei-kang;CHENG Hui;SHI Kai(Nanjing Institute of Technology, Nanjing 210000, China)

机构地区：[1]南京工程学院,江苏南京210000

出　　处：《计算机与现代化》2022年第3期7-12,共6页Computer and Modernization

基　　金：江苏省研究生科研与实践创新计划项目(SJCX19_0526)。

摘　　要：为了提高短期负荷预测的准确度,降低非平稳信号对模型预测造成的影响,提出一种集数据挖掘和多目标优化网络于一体的短期负荷预测模型。该方法将电力负荷数据通过变分模态分解技术分解成若干个不同频率的模态分量,采用相间重构方法动态确定人工神经网络的训练测试比和神经元设置,采用长短期记忆神经网络对各分量进行模型搭建,并在长短期记忆神经网络的基础上加入多目标蝗虫智能优化算法来优化网络内部参数,累加所有分量模型预测的值,实现短期负荷预测。仿真结果表明,与统计学方和混合模型相比,本文提出的模型在短期预测方面的预测精度较高、泛化能力更强。In order to improve the accuracy of short-term load forecasting and reduce the influence of non-stationary signals on model forecasting,this paper proposes a short-term load forecasting model that integrates data mining and multi-objective optimization networks.This method decomposes the power load data into several modal components with different frequencies through variational mode decomposition(VMD)technology,uses the phase space reconstruction(PSR)method to dynamically determine the training of the artificial neural network test ratio and neuron settings,uses the long short-term memory(LSTM)neural network to build models for each component,adds multi-objective grasshopper optimization algorithm(MOGOA)on the basis of LSTM to optimize the internal parameters of the network,and accumulates the predicted values of all component models to realize short-term load forecasting.The simulation results show that,compared with the statistical method and the hybrid model,the proposed model has higher prediction accuracy and stronger generalization ability in short-term prediction.

关 键 词：变分模态分解 相间重构 LSTM 多目标蝗虫优化算法 短期负荷预测 

分 类 号：TM719[电气工程—电力系统及自动化]