控制与信息技术Issue(3)：54-60,7.DOI:10.13889/j.issn.2096-5427.2025.03.007
Periodformer——基于时间序列分解的能耗预测模型
Periodformer:An Energy Consumption Prediction Model Based on Decomposition of Time Series
摘要
Abstract
In recent years,deep learning technology has demonstrated remarkable potential across various prediction tasks.However,existing deep learning models still fall short in fully exploiting the periodicity,trends,and residual characteristics inherent in energy consumption data.To address these deficiencies,this paper proposes a novel prediction model called Periodformer.The model begins by decomposing time series into three components:trend,period,and residual.Each component is modeled separately,and the prediction results from these models are then integrated,leading to significantly improved prediction accuracy.Experimental results showed that Periodformer achieved reductions in both Mean Absolute Error(MAE)and Mean Squared Error(MSE)of 5.56%and 11.85%,respectively,compared to the existing Transformer model,while exhibiting strong robustness against data noise.关键词
列车能耗预测/时间序列分解/Transformer/Periodformer/深度学习模型Key words
train energy consumption prediction/time series decomposition/Transformer/Periodformer/deep learning models分类
交通工程引用本文复制引用
陈波文,邓健,朱乾鎏..Periodformer——基于时间序列分解的能耗预测模型[J].控制与信息技术,2025,(3):54-60,7.基金项目
中国国家铁路集团有限公司科技研究开发计划项目(N2024J032-B(JB)) （N2024J032-B(JB）
