This paper presented an improved self-adaptive particle swarm optimization (IDPSO) algorithm with detection function to solve multimodal function optimization problems. To overcome the premature convergence of PSO in a short time, the evolution direction of each particle is redirected dynamically by tuning the three parameters of IDPSO in the evolution process. Numerical results on several benchmark functions indicate that the IDPSO strategy outperformed other variants of PSO.

Wind energy is a clean energy source and is receiving widespread attention. Improving the operating efficiency and economic benefits of wind power generation systems depends on more accurate short-term wind speed predictions. In this study, a new hybrid model for short-term wind speed forecasting is proposed. The model combines variational modal decomposition (VMD), the proposed improved seagull optimization algorithm (ISOA) and the kernel extreme learning machine (KELM) network. The model adopts a hybrid modeling strategy: firstly, VMD decomposition is used to decompose the wind speed time series into several wind speed subseries. Secondly, KELM optimized by ISOA is used to predict each decomposed subseries. The ISOA technique is employed to accurately find the best parameters in each KELM network such that the predictability of a single KELM model can be enhanced. Finally, the prediction results of the wind speed sublayer are summarized to obtain the original wind speed. This hybrid model effectively characterizes the nonlinear and nonstationary characteristics of wind speed and greatly improves the forecasting performance. The experiment results demonstrate that: (1) the proposed VMD-ISOA-KELM model obtains the best performance for the application of three different prediction horizons compared with the other classic individual models, and (2) the proposed hybrid model combining the VMD technique and ISOA optimization algorithm performs better than models using other data preprocessing techniques.

A spatial quality control method, ARF, is proposed. The ARF method incorporates the optimization ability of the artificial fish swarm algorithm and the random forest regression function to provide quality control for multiple surface air temperature stations. Surface air temperature observations were recorded at stations in mountainous and plain regions and at neighboring stations to test the performance of the method. Observations from 2005 to 2013 were used as a training set, and observations from 2014 were used as a testing set. The results indicate that the ARF method is able to identify inaccurate observations; and it has a higher rate of detection, lower rate of change for the quality control parameters, and fewer type I errors than traditional methods. Notably, the ARF method yielded low performance indexes in areas with complex terrain, where traditional methods were considerably less effective. In addition, for stations near the ocean without sufficient neighboring stations, different neighboring stations were used to test the different methods. Whereas the traditional methods were affected by station distribution, the ARF method exhibited fewer errors and higher stability. Thus, the method is able to effectively reduce the effects of geographical factors on spatial quality control.

The safe operation of high-speed railways (HSRs) is significantly challenged by strong winds. Accurate wind speed prediction along HSRs is crucial for ensuring the safety of train operations. However, existing research primarily focuses on designing and improving data-driven models, with limited attention given to the characteristics of wind speed specific to HSR environments. To address this gap, this study analyzes the wind speed characteristics of weather stations (WSs) and railway stations (RSs) along an HSR. These characteristics are explored from multiple perspectives, including wind speed variability, amplitude, correlation, wind speed distribution, and turbulence across different time scales. Additionally, the normalized cumulative periodogram (NCP) and Bartlett’s test are employed to quantify wind speed predictability. A wind speed prediction model is then developed based on predictability analysis. The findings reveal that RS wind speeds differ significantly from WS wind speeds, exhibiting higher volatility. The predictability of wind speed is influenced by the sampling interval: as the sampling time increases, the predictability and length of the predictable historical wind speed period decrease. By establishing a prediction model grounded in wind speed predictability analysis, irrelevant historical wind speed data can be excluded, improving the model’s prediction accuracy. Predictability analysis thus provides a robust foundation for forecasting strong winds along HSRs, ultimately enhancing train operation safety.