A probabilistic approach for short-term prediction of wind gust speed using ensemble learning

Strong winds could cause train derailment and truck rollover which may result in service interruption, serious injury, and even loss of life. The wind-induced accident is highly related to the maximum value of short-term wind speed, thus highlighting the importance of regulating the vehicle velocity based on wind gusts. Accurate prediction of wind gusts is essential to control the vehicle velocity ahead of time, thereby reducing the risk of accidents. The majority of existing approaches focus on the prediction of mean wind speed. In contrast, fairly limited research applies the machine learning model to forecast wind gusts with strong time-varying characteristics and volatility. In this study, a probabilistic approach is presented to forecast wind gusts using ensemble learning. The ensemble model includes three machine learning models, namely, random forest (RF), long-short term memory (LSTM), and Gaussian process regression (GPR) model. The proposed probabilistic approach allows for the quantification of uncertainty in prediction of wind gusts. The feasibility of the ensemble model is illustrated by using the field wind measurements acquired from a long-span cable-stayed bridge. Compared to the persistence, RF, LSTM, GPR, averaging, and gradient boosting decision tree models, the proposed ensemble model exhibits higher accuracy and generalization performance. © 2020 Elsevier Ltd