Short-term air conditioning load prediction model based on optimized LSTM hyperparameters

This paper proposes a hybrid prediction algorithm with a long short-term memory (LSTM) network optimized by the improved information acquisition optimizer (IIAO) based on the opposition-based learning (OBL) strategy. Firstly, the Spearman correlation coefficient method is used to select features highly correlated with air conditioning loads. Subsequently, the hyperparameters of the LSTM model, including learning rate and L2 regularization coefficient, are optimized using the IIAO algorithm to obtain the optimal combination of hyperparameters, and the IIAO-LSTM air conditioning load prediction model is constructed. Finally, this model is applied to the air conditioning load prediction of a university laboratory in Xi’an city, and is compared with other prediction models. Experimental results show that the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the IIAO-LSTM model in predicting air conditioning load are 1.05% and 3.71 kW, respectively, and the model running time is 23.33 seconds. It has higher prediction accuracy and shorter running time, and has strong generalization ability. It is suitable for predicting air conditioning loads with strong temporal characteristics.