Acquisition method of artificial internal disturbance feature variables for short-term prediction model of building cooling load based on model calibration

Accurate prediction of short-term building cooling load is of great significance to the operation optimization of building energy supply systems. Data-driven models have been widely used due to their advantages in mining the actual building load characteristics and improving the prediction accuracy. However, the absence of internal disturbance feature variables seriously affects the prediction effect of data-driven load prediction models. Therefore, this paper proposes a method to mine internal disturbance data from cooling load time series using model calibration techniques. The case study results show that the data of artificial internal disturbance feature variables obtained by this method can significantly improve the prediction effect of short-term building cooling load using the artificial neural network model. The prediction error can be reduced by 11.46% compared to the prediction model without internal disturbance feature variables, and by 6.51% compared to the prediction model using calendar information as internal disturbance feature variables.