High-precision air conditioning load forecasting model based on improved sparrow search algorithm

Abstract

Air conditioning load prediction is a crucial prerequisite for ensuring the efficient operation of air conditioning systems and enhancing demand-side response in electricity. Due to the uncertainty in model parameter settings and limitations of the Sparrow Search Algorithm (SSA2) itself, leading to significant human and material resources consumption during the prediction process, this study improves SSA (ISSA) to enhance air conditioning prediction efficiency and accuracy. The enhanced SSA is then combined with Long Short-Term Memory (LSTM). Firstly, Singular Spectrum Analysis (SSA1) is employed for feature extraction on the load to obtain more regular sub-signals, facilitating subsequent prediction tasks. Simultaneously, the Maximum Information Coefficient (MIC) is used to measure the correlation between data, assisting in selecting input variables for the model. Subsequently, each sub-signal is input into the ISSA-LSTM model for prediction, and the prediction results of each sub-signal are reconstructed to obtain the final prediction. Finally, data from air conditioning loads in two different types of buildings are collected to validate the model. Upon validation, it was found that the SSA1-ISSA- LSTM model exhibits superior performance compared to other advanced combination models. When predicting air conditioning loads in office building, the R2 value reaches 0.9955, with MAPE, MAE, and RMSE being 0.73 %, 4.658RT, and 6.1151RT respectively. When predicting air conditioning loads in medical building, the R2 is as high as 0.9920, with MAPE, MAE, and RMSE being 0.55 %, 5.8543RT, and 7.9599RT respectively.