A hybrid forecasting method for cooling load in large public buildings based on improved long short term memory

Abstract

Air conditioning system in large public buildings have great potential for energy saving. In order to improve the operational efficiency of the air conditioning system, it is crucial to establish an accurate and effective cooling load forecasting model. This paper proposes a hybrid model based on random forest-improved sparrow search algorithm-long short term memory (RF-ISSA-LSTM) for forecasting the cooling load of large public buildings. Firstly, random forest (RF) is used to select high-impact parameters for building cooling load and reduce the dimensionality of the original input data. Secondly, three strategies are introduced to improve the location update in different stages of the sparrow search algorithm (ISSA), and then an ISSA-optimized LSTM forecasting model is established. Finally, two case studies are selected to evaluate the predictive performance of the model in detail. The experimental results show that the mean average percentage error (MAPE) and root mean square error (RMSE) of the RF-ISSA-LSTM model predicting the cooling load for these two buildings are 0.6072%, 3.3552 and 0.7939%, 4.4227, respectively. Compared with other neural network models, the RF-ISSA-LSTM model has higher prediction accuracy and shorter running time. Meanwhile, with the reduced training sample size, the RF-ISSA-LSTM model can still effectively predict the cooling load, which indicates its strong generalization ability. Therefore, this work is enlightening and the proposed hybrid prediction model can serve as a technical tool to support the energy efficiency management of large public buildings.