信息与控制2024，Vol.53Issue(2)：238-249,12.DOI:10.13976/j.cnki.xk.2024.3021
基于VMD-CNN-LSTM的农业大棚园区用电负荷短期预测
The Short-term Forecasting of Power Load in Agricultural Greenhouses Based on VMD-CNN-LSTM
摘要
Abstract
Based on the meteorological and temporal characteristics of short-term load in greenhouses,an integrated short-term load prediction architecture based on the fusion of variational mode decompo-sition(VMD),convolutional neural network(CNN),and long short-term memory(LSTM)is proposed to solve the problem that the electricity load of agricultural greenhouses is affected by ru-ral power supply capacity and meteorological factors,which have strong fluctuation and high non-linear characteristics.First,the load sequence is decomposed using the VMD method to reduce load volatility.Second,the meteorological characteristics of the load are extracted using the CNN method,and the temporal characteristics of the load are extracted using the LSTM method.Compo-nent prediction is performed,and the prediction results of the model components are reconstructed.Finally,based on the load data of agricultural greenhouses in Shouguang,Shandong Province,the experimental results show that compared with the traditional neural network model,the proposed VMD-CNN-LSTM model can effectively improve the precision of short-term load prediction of agri-cultural greenhouses.关键词
农业大棚园区负荷/变分模态分解/短期负荷预测/卷积神经网络/长短期记忆网络Key words
agricultural greenhouse park load/variational mode decomposi-tion/short-term load forecasting/convolutional neural network/long short-term memory net-work分类
信息技术与安全科学引用本文复制引用
张培霄,尹晓红,李少远,王新立..基于VMD-CNN-LSTM的农业大棚园区用电负荷短期预测[J].信息与控制,2024,53(2):238-249,12.基金项目
国家自然科学基金项目(61703223) （61703223）
山东省重点研发计划重大科技创新工程项目(2020CXGC011402) （2020CXGC011402）
