Hybrid forecasting model of building cooling load based on EMD-LSTM-Markov algorithm

Abstract

Precise forecasting of the cooling load (CL) of buildings is crucial for the efficient functioning of central air conditioning systems. The study proposed a new hybrid model based on empirical modal decomposition (EMD) and Markov chain improved LSTM neural network, denoted as EMD-LSTM-Markov. First, Pearson’s correlation coefficient (PCC) and Gradient Boosting Decision Tree (GBDT) are used to extract features with a high degree as the input data. Then, the LSTM is employed to establish a prediction model. Finally, to verify the validity and prediction accuracy of the proposed model, this paper selects the CL data of an office building in Guangzhou as the research sample. The results show that the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the EMD-LSTM-Markov model are 4.53 and 0.84%. Compared with the other three predicting models, the RMSE and MAPE values of EMD-LSTM-Markov are decreased by 40% − 94% and 70% − 96%, which has better performance in accuracy and stability. It testifies that the proposed strategy is reliable and effective. This work successfully resolves the issue of delayed cold response. The hybrid model proposed demonstrates significant energy-saving potential for accurately predicting building CLs and enhancing the optimal control of cooling systems. Future research should explore the hybrid model’s robustness across different building types and climatic regions.