现代电力2024，Vol.41Issue(1)：97-105,9.DOI:10.19725/j.cnki.1007-2322.2022.0210
基于变分模态分解的卷积长短时记忆网络短期电力负荷预测方法
Short-term Power Load Forecasting Method Based on Variational Modal Decomposition for Convolutional Long-short-term Memory Network
摘要
Abstract
The power load sequence is complicated and easily affected by multiple external factors,making it difficult to anti-cipate with accuracy.A parallel forecasting method of short-term power load combining variational modal decomposition(VMD)and convolutional neural network and long short-term memory network(CNN-LSTM)is proposed to address the problem.Firstly,VMD is adopted to decompose the load data into various intrinsic mode functions(IMF)with strong regular-ity and residual error;Secondly,the obtained components are input into the corresponding CNN-LSTM hybrid prediction net-work to obtain each initial prediction value,and combine this value with the correlation factor feature set obtained by com-bining climate,date type,etc.to further obtain the revised pre-diction value;Finally,the revised prediction values of each component are superimposed to obtain a complete prediction result.According to the simulation on the actual load data,the average relative error of daily load forecasting can be reduced by 2.18%after taking into about the relevant external factor features set.In addition,compared with several conventional load forecasting methods,the effectiveness and feasibility of the proposed method can be verified.关键词
短期负荷预测/变分模态分解/卷积神经网络/长短期记忆网络/相关因素特征集Key words
short-term load forecasting/variational mode de-composition/convolutional neural network/long short-term memory network/correlation factor feature set分类
信息技术与安全科学引用本文复制引用
黄睿,朱玲俐,高峰,王渝红,杨亚兰,熊小峰..基于变分模态分解的卷积长短时记忆网络短期电力负荷预测方法[J].现代电力,2024,41(1):97-105,9.基金项目
四川省科技计划资助项目(2021YFG0026).Project Supported by Science and Technology Program of Sichuan Province(2021YFG0026). （2021YFG0026）
