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.

This paper considers the downlink resource allocation of a coordinated multi-cell cluster in OFDMA systems with universal frequency reuse. Multi-cell joint transmission is considered via zero-forcing precoding. Furthermore, joint optimization of the user selection and power allocation across multiple subchannels and multiple cells is studied. The objective is to maximize the weighted sum rate under per-base-station power constraints. Based on general duality theory, two iterative resource allocation algorithms are proposed and compared with the optimal solution, which requires an exhaustive search of all possible combinations of users over all subchannels. Simulation results show that the two proposed algorithms achieve a performance very close to the optimal, with much lower computational complexity. In addition, we show that joint user set selection across multiple subchannels significantly improves the system performance in terms of the weighted sum rate.

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.

An improved kernel regression (IKR) method based on an adaptive algorithm and particle swarm optimization is proposed. Considering the limitations of current quality control methods in different regions and on multiple time scales, the kernel regression algorithm is applied to the quality control of surface air temperature observations. Observations of 12 reference stations in Jiangsu from 1961 to 2008 and of 14 regions in China from 2010 to 2014 were selected. The analysis of surface air temperature observations was performed in terms of the mean absolute error (MAE), root mean square error (RMSE), consistency indicator (IOA), and Nash–Sutcliffe model efficiency coefficient (NSC). The results indicate that compared with the traditional IDW and SRT methods, the IKR method has a high error detection rate. Furthermore, the IKR method achieves better predictions and fitting in the single-station and multistation regression experiments in Jiangsu and in the national multistation regression prediction experiment.

Meteorological observations are essential for climate modelling, prediction, early warning systems, decision-making processes, and disaster management. These observations are critical to societal development and the safeguarding of human activities and livelihoods. Spatial interpolation techniques play a pivotal role in addressing gaps between observation sites, enabling the generation of continuous meteorological datasets. However, due to the inherent complexity of atmosphere–surface interactions, no single interpolation technique has proven universally effective in achieving consistently accurate results for meteorological variables. This study proposes a novel interpolation model based on Fuzzy Adaptive Optimal Fusion (FAOF). The FAOF model integrates fuzzy theory by constructing station-specific fuzzy sets and sub-method element pools, employing a nonlinear membership function with error as the independent variable. An iterative accuracy index is used to identify the optimal parameter combination, facilitating adaptive data fusion and interpolation optimisation. The model’s performance is evaluated against 10 individual methods from the method pool. Experimental results demonstrate that FAOF effectively combines the strengths of multiple methods, achieving significantly enhanced interpolation accuracy. Additionally, the model consistently performs well across diverse regions and meteorological variables, underscoring its robustness and strong generalisation capability.