An Indoor Temperature Prediction Framework Based on Hierarchical Attention Gated Recurrent Unit Model for Energy Efficient Buildings

Indoor temperature is an important criterion for evaluating the operation quality of district heating systems (DHSs) and has a significant impact on improving the energy efficiency of heat exchange station regulation. Accurate prediction of indoor temperature is of great help to the precise control of heating systems. However, due to the thermal inertia and response delay characteristics of heating systems, there is a complex nonlinear relationship between indoor temperature and its many related factors. A hierarchical attention gated recurrent unit (HAGRU) neural network is innovatively proposed for predicting the indoor temperature of energy-saving buildings in which the indoor temperature is optimally regulated based on a newly designed smart on-off valve. The network is divided into two levels of attention model, which can realize the representation of a single influencing factor and the fusion of multiple feature inputs. Detailed simulation results show that the predictive accuracy of the proposed algorithm is 98.4%, which is significantly better than state-of-art algorithms, such as support vector machine (SVM), random forest regression (RFR), decision tree regression (DTR), gradient boosting regression (GBR), recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent units (GRU). Therefore, the proposed HAGRU algorithm has good nonlinear feature extraction and expression ability. The indoor temperature prediction results are used as feedback input to assist the control strategy of the heat exchanger station, which is conducive to the improvement of energy efficiency. In addition, the optimal selection of hyperparameters of the HAGRU algorithm is also analyzed in detail.