Distributed LSTM-GCN-Based Spatial–Temporal Indoor Temperature Prediction in Multizone Buildings

Abstract

Indoor temperature prediction of multiple zones in near future horizons is vital in developing an optimal regulation strategy of heating, ventilation, and air conditioning systems in large-scale complex buildings. This is, however, challenging due to the spatial–temporal correlation and multivariable coupling characteristics. This article proposes a novel deep learning framework incorporating the distributed long short-term memory and graph convolution network namely DL-GCN for indoor temperature prediction in large public buildings, aiming to learn the spatial–temporal correlation and multivariable coupling features. First, the indoor temperature and humidity data from different zones are handled by GCN networks to extract the temperature spatial features. Then, in the distributed LSTM module, other data, such as light and ac power consumption, are fused with the outputs of the GCN module, respectively, in a distributed way to learn the coupling interactions and temporal characteristics between these variables. Comparison study and ablation experiments are conducted using real datasets from a large-scale building to verify its effectiveness and superior performance in multizone indoor temperature prediction.