Short-term air conditioning load forecasting model based on SSAn-SSAl-LSTM

In this paper,a combined air conditioning load forecasting model based on long short-term memory network (LSTM) optimized by singular spectrum analysis (SSAn) and sparrow search algorithm (SSAl) is proposed.The Pearson correlation coefficient and the principal component analysis are used to select and process the input features to eliminate the redundancy and correlation between the features.In response to the volatility and randomness of the air conditioning load,SSAnis used to decompose the air conditioning load into multiple components.At the same time,aiming at the problem of LSTM hyperparameter setting,SSAlis used to optimize the model,and the optimized LSTM is used to predict each component.The prediction results are reconstructed.The model is validated and analysed using the air conditioning load data from office and medical buildings.It is found that the SSAn-SSAl-LSTM model performs the best compared with other models.When predicting the air conditioning load of the office building,the coefficient of determination (R2) is as high as 0.996 7,the average absolute percentage error (MAPE),the average absolute error (MAE) and the root mean square error (RMSE) are 0.62%,14.42 kW and 18.82 kW,respectively.When predicting the air conditioning load of the medical building,R2is 0.992 7,MAPE,MAE and RMSE are 0.5%,19.40 kW and 25.71 kW,respectively.