A novel graph convolutional network-based interpretable method for chiller energy consumption prediction considering the spatiotemporal coupling between variables

The prediction of chiller energy consumption is critical for lowering the building's energy consumption. Deep neural networks have been widely applied in this field. However, traditional deep learning methods only consider operational data and do not incorporate empirical knowledge and working condition information that are beneficial for chiller energy consumption prediction. The graph convolutional network (GCN) can handle this problem well, as it is able to incorporate empirical knowledge and working condition information into the network. However, it is important to avoid the excessive influence of empirical knowledge and working condition information on the model's accuracy. To adjust the weight of the influence of the association graph and training set on model accuracy in the GCN network, a coefficient θ was introduced. Herein, a novel GCN (θ-GCN)-based method for the energy consumption prediction of chillers is proposed. This proposed method first combines empirical knowledge and working condition information to obtain the adjacency matrix among operational data and then constructs an association graph based on this adjacency matrix. Secondly, due to the temporal correlation of the collected dataset, a gated recurrent unit (GRU) is used for feature extraction. Lastly, the features extracted through GRU are input into the constructed association graph, and the θ-GCN network is used for training and energy consumption prediction. The proposed method was experimentally verified in an actual chiller system and compared with existing methods; the outcomes indicated that the established method could obtain better prediction accuracy.