广西师范大学学报（自然科学版）2024，Vol.42Issue(5)：39-51,13.DOI:10.16088/j.issn.1001-6600.2023082702
基于VMD和RDC-Informer的短期供热负荷预测模型
Short-Term Heating Load Prediction Model Based on VMD and RDC-Informer
摘要
Abstract
Accurate prediction of heating load not only effectively reduces energy consumption but also improves the efficiency of the heating system and user comfort.To enhance the accuracy of heating load prediction,this study combines the Variational Mode Decomposition(VMD)algorithm with an improved Informer model for heating load prediction.Firstly,the VMD algorithm is used to decompose the heating load data,reducing its non-stationarity.Secondly,relative positional encoding is introduced in the Informer model to better capture the dependencies in the sequential data and avoid information leakage,replacing the absolute positional encoding.Furthermore,dilated causal convolution is adopted instead of regular convolution to increase the receptive field and enhance the extraction of local information.Comparative experiments with mainstream prediction models(GRU,LSTM,Transformer,and Informer)are conducted on multiple datasets.The experimental results demonstrate that the RDC-Informer model achieves an R2 evaluation metric of 98.3％,which is 11.6％,6.3％,4.7％,and 2.6％higher than the comparative models,respectively.Additionally,the effectiveness of the dilated causal convolution is verified by increasing the convolution kernel,confirming the applicability and accuracy of the RDC-Informer model and providing a reference for further improvement in real-time smart heating.关键词
供热负荷预测/Informer/膨胀因果卷积/相对位置编码/VMDKey words
heat supply load forecast/Informer/dilated causal convolution/relative position coding/VMD分类
建筑与水利引用本文复制引用
谭全伟,薛贵军,谢文举..基于VMD和RDC-Informer的短期供热负荷预测模型[J].广西师范大学学报（自然科学版）,2024,42(5):39-51,13.基金项目
河北省自然科学基金(E2020209121) （E2020209121）
