Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In...Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.展开更多
Accurately predicting the chiller coefficient of performance(COP)is essential for improving the energy efficiency of heating,ventilation,and air conditioning(HVAC)systems,significantly contributing to energy conservat...Accurately predicting the chiller coefficient of performance(COP)is essential for improving the energy efficiency of heating,ventilation,and air conditioning(HVAC)systems,significantly contributing to energy conservation in buildings.Traditional performance prediction methods often overlook the dynamic interaction among sensor variables and face challenges in using extensive historical data efficiently,which impedes accurate predictions.To overcome these challenges,this paper proposes an innovative on-site chiller performance prediction method employing a dynamic graph convolutional network(GCN)enhanced by association rules.The distinctive feature of this method is constructing an association graph bank containing static graphs in each operating mode by mining the association rules between various sensor variables in historical operating data.A real-time graph is created by analyzing the correlation between various sensor variables in the current operating data.This graph is fused online with the static graph in the current operating mode to obtain a dynamic graph used for feature extraction and training of GCN.The effectiveness of this method has been empirically confirmed through the operational data of an actual building chiller system.Comparative analysis with state-of-the-art methods highlights the superior performance of the proposed method.展开更多
文摘Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.
基金supported in part by the Science and Technology Innovation Program of Hunan Province(No.2022RC1090)in part by the National Natural Science Foundation of China(No.62173349)+2 种基金in part by the Natural Science Foundation of Hunan Province(No.2022J20076)in part by the Innovation Driven Projection of Central South University(No.2023CXQD073)in part by the Major Program of Xiangjiang Laboratory(No.22XJ01005).
文摘Accurately predicting the chiller coefficient of performance(COP)is essential for improving the energy efficiency of heating,ventilation,and air conditioning(HVAC)systems,significantly contributing to energy conservation in buildings.Traditional performance prediction methods often overlook the dynamic interaction among sensor variables and face challenges in using extensive historical data efficiently,which impedes accurate predictions.To overcome these challenges,this paper proposes an innovative on-site chiller performance prediction method employing a dynamic graph convolutional network(GCN)enhanced by association rules.The distinctive feature of this method is constructing an association graph bank containing static graphs in each operating mode by mining the association rules between various sensor variables in historical operating data.A real-time graph is created by analyzing the correlation between various sensor variables in the current operating data.This graph is fused online with the static graph in the current operating mode to obtain a dynamic graph used for feature extraction and training of GCN.The effectiveness of this method has been empirically confirmed through the operational data of an actual building chiller system.Comparative analysis with state-of-the-art methods highlights the superior performance of the proposed method.
