Multi-zone indoor temperature prediction based on Graph Attention Network and Gated Recurrent Unit

Abstract

Indoor temperature have significant influence on load forecasting, comfort control and security monitoring. Achieving accurate temperature prediction can provide key basic data for energy efficiency and building safety and comfort. In the case of multiple zones, the heat transfer process in adjacent zones can have an important impact on the dynamics of indoor temperature. This paper focuses on the influence of heat transfer process in multiple adjacent zones. To describe the interactions of temperature among the multiple zones, we consider the zones as nodes and the connected walls as edges based on actual layouts to construct the graph network. For the non-linearity of the heat transfer process, we propose a novel multi-zone indoor temperature prediction model based on graph attention mechanism and recurrent network to achieve one-step ahead and multi-step ahead temperature predictions. The accuracy of this model was further verified by using data generated from EnergyPlus simulations. The best predicted result had an RMSE value of 0.47, an MAE value of 0.37, and an R2 value of 0.94.