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a b s t r a c tWind speed high-precision prediction is one of the most important technical aspects to protect the safety of wind power utilization. In this study, two new hybrid methods [FEEMD-MEA-MLP/FEEMD-GA-MLP] are proposed for the wind speed accurate multi-step predictions by combining FEEMD (Fast Ensemble Empirical Mode Decomposition), MEA (Mind Evolutionary Algorithm), GA (Genetic Algorithm) and MLP (Multi Layer Perceptron) neural networks. In these two hybrid methods, the FEEMD algorithm is adopted to decompose the original wind speed series into a number of sub-layers and the MLP neural networks optimized by the MEA algorithm and the GA algorithm are built to predict the decomposed wind speed sub-layers, respectively. The innovation of the study is to investigate the promoted percentages of the MLP neural networks by the FEEMD decomposition and the MEA/GA optimization, respectively. The involved forecasting models in the performance comparison in the study include the hybrid FEEMD-MEA-MLP, the hybrid FEEMD -GA-MLP, the hybrid FEEMD-MLP, the hybrid MEA-MLP, the hybrid GA-MLP and the single MLP. Two experimental results show that: (a) among all the involved methods, the hybrid FEEMD-MEA-MLP model has the best forecasting performance; (b) the FEEMD algorithm promotes the performance of the MLP neural networks significantly while the MEA/GA algorithms do not improve the performance of the MLP neural networks significantly; and (c) the hybrid FEEMD-MEA-MLP method and the hybrid FEEMD-GA-MLP method are both effective in the wind speed high-precision predictions.

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The focus of this study was the influence of the length of a train on its aerodynamic performance, with and without wind break walls, under a crosswind. The improvement in the train's aerodynamic performance due to a wind break wall was also analyzed. Aerodynamic coefficients such as the drag force and lateral force were obtained for trains of different lengths using a numerical simulation. A delayed detached eddy simulation based on the shear-stress transport κ-ω turbulent model was used in Fluent 14.0 to simulate the unsteady aerodynamic performances of trains of different lengths under a crosswind. Through a comparison and analysis of the simulation results, the effects of the train length on the forces, pressure, and flow structure around the train were studied, along with the influence of a wind break wall on trains of different lengths. The results showed that the effects on the forces, pressure, and flow structure were focused in the region around the train tail. Furthermore, it was found that a wind break wall could improve the aerodynamic characteristics of a train under a crosswind, but amplified the influence of the train length on the aerodynamic performance of the train tail. These research results provide useful guidance for train operations under a crosswind. ARTICLE HISTORY