An EMD-recursive ARIMA method to predict wind speed for railway strong wind warning system

Abstract

To protect running trains against the strong crosswind along Chinese Qinghai–Tibet railway, a strong wind warning system is developed. As one of the most important technologies of the developed system, a new short-term wind speed forecasting method is proposed by adopting the Empirical Mode Decomposition (EMD) and the improved Recursive Autoregressive Integrated Moving Average (RARIMA) model. The proposed forecasting method consists of three computational steps as: (a) use the EMD method to decompose the original wind speed data into a group of wind speed sub-layers; (b) build the forecasting models for all the decomposed sub-layers by utilizing the RARIMA algorithm; (c) employ the built RARIMA models to predict the wind speed in the sub-layers; and (d) summarize the predicted results of the wind speed sub-layers to get the final forecasting results for the original wind speed. Since the wind speed forecasting method is proposed for the real-time warning system, the forecasting accuracy and the time performance of the forecasting computation are both considered. Two experiments show that: (a) the proposed method has better forecasting performance than the traditional Autoregressive Integrated Moving Average (ARIMA) model, the Persistent Random Walk Model (PRWM) and the Back Propagation (BP) neural networks; and (b) the proposed method has satisfactory performance in both of the accuracy and the time performance.