热负荷预测模型输入变量选择及预测性能评价OA

The input variables of building heat load fore-casting model were determined by correlation analysis,and the building heat load simulated by DeST software was used as sample data to establish a long-term and short-term memory(LSTM)network-based building heat load prediction model.The optimal hyperparam-eter combination of the LSTM network was selected.The input variables of the building heat load forecast-ing model include dry-bulb temperature,relative hu-midity,horizontal total radiation,occupancy status(oc-cupied or not),historical heat load at the 1st hour,his-torical heat load at the 2nd hour,and historical heat load at the 24th hour.The optimal hyperparameter combination is 16 nodes,learning rate of 0.001,batch size of 4,and 30 iterations.The LSTM network-based building heat load forecasting model demonstrates high prediction accuracy and excellent forecasting perfor-mance.