Load forecasting model based on ARIMA and CART

Abstract

The load forecasting of host resources is of great significance to the operation and maintenance work. The traditional load forecasting methods usually use linear model to fit the load data. In the actual operation of equipment, the load is affected by the complex internal and external environment where many nonlinear factors are included. The linear time series model can not well characterize the law of load data. In order to improve the model accuracy, the idea of decomposing the load information into linear part and nonlinear parts is proposed, and the autoregressive integrated moving average (ARIMA) model and the classification and regression tree (CART) model are combined for prediction. Specifically, the ARIMA model improved by the weighted least squares method is used to predict the linear part and the CART optimized by the boundary determination is used to predict the nonlinear part, and the prediction results of the two parts are combined to obtain a comprehensive prediction result. The comparison experiments are carried out on the real load dataset. The results show that the prediction accuracy of the proposed algorithm is improved by more than 15% compared with the traditional method, and it has good adaptability to remote values and different time intervals.