广西师范大学学报（自然科学版） ›› 2024, Vol. 42 ›› Issue (5): 39-51.doi: 10.16088/j.issn.1001-6600.2023082702

• 研究论文 • 上一篇    下一篇

基于VMD和RDC-Informer的短期供热负荷预测模型

谭全伟, 薛贵军*, 谢文举   

  1. 华北理工大学 电气工程学院,河北 唐山 063200
  • 收稿日期:2023-08-27 修回日期:2023-11-30 出版日期:2024-09-25 发布日期:2024-10-11
  • 通讯作者: 薛贵军(1967—),男,河北唐山人,华北理工大学高级工程师。E-mail: xueguijun@126.com
  • 基金资助:
    河北省自然科学基金(E2020209121)

Short-Term Heating Load Prediction Model Based on VMD and RDC-Informer

TAN Quanwei, XUE Guijun*, XIE Wenju   

  1. School of Electrical Engineering, North China University of Science and Technology, Tangshan Heibei 063200, China
  • Received:2023-08-27 Revised:2023-11-30 Online:2024-09-25 Published:2024-10-11

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摘要： 精准的供热负荷预测不仅可以有效降低能源消耗,而且可以提高供热系统效率和用户舒适度。为了提升供热负荷预测的准确性,本文将变分模态分解算法和改进的Informer模型结合应用于供热负荷预测中。首先使用VMD算法分解供热负荷数据,降低数据的非平稳性;然后在Informer模型中引入相对位置编码代替绝对位置编码,以更好地捕捉序列数据中的依赖关系和避免信息泄漏;接着采用膨胀因果卷积代替正则卷积,增加感受野,提升局部信息的提取能力;最后在多个数据集上与主流预测模型(GRU、LSTM、Transformer和Informer)进行对比实验。结果表明,RDC-Informer模型的评价指标R2达到了98.3%,与对比模型相比,分别提高了11.6%、6.3%、4.7%和2.6%。此外,通过增加卷积核以评估膨胀因果卷积的效果,验证了RDC-Informer模型的适用性和准确性,为进一步提高智慧供热的时效性提供了一定参考。

关键词: 供热负荷预测, Informer, 膨胀因果卷积, 相对位置编码, VMD

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.

Key words: heat supply load forecast, Informer, dilated causal convolution, relative position coding, VMD

中图分类号:  TU995

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