A performance prediction method for on-site chillers based on dynamic graph convolutional network enhanced by association rules

Abstract

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.