电力需求侧管理2025，Vol.27Issue(5)：23-29,7.DOI:10.3969/j.issn.1009-1831.2025.05.004
基于多元时序解耦多模态学习的农业负荷预测模型
Agricultural load forecasting model based on multivariate temporal decoupling and multimodal learning
摘要
Abstract
As the agricultural load is greatly affected by meteorological factors and a single decomposition method cannot fully extract the multidimensional features existing between multiple inputs,an agricultural load forecasting model based on multivariate variational mode decomposition combined with SVR Bi GRU TCN combined model is proposed.Firstly,using multivariate variational mode decomposition to adaptively decompose historical agricultural loads and meteorological characteristics,real-time mining of modal components with differ-ent feature scales between data is carried out.Then,based on the inherent properties of each modal component,SVR,Bi-GRU,and TCN models are established to extract feature information at different time scales,thereby achieving accurate prediction of future 1-hour agricul-tural loads.The experimental results show that compared with the SVR model,Bi-GRU model,and TCN model,LSTM model and CNN-Bi-LSTM model,the proposed prediction model can effectively improve the prediction accuracy.关键词
农业负荷/深度学习/多元变分模态分解/时间卷积神经网络/双向门控循环单元Key words
agricultural load/deep learning/multivariate variational mode decomposition/temporal convolutional network/bidirectional gated recurrent unit分类
信息技术与安全科学引用本文复制引用
勇晔,薛溟枫,毛晓波..基于多元时序解耦多模态学习的农业负荷预测模型[J].电力需求侧管理,2025,27(5):23-29,7.基金项目
国家电网有限公司科技项目(5400-202318246A-1-1-ZN) （5400-202318246A-1-1-ZN）
