Forecasting

This study employed tree-based machine learning (ML) algorithms to predict low-level wind shear (LLWS) at Jeju International Airport (ICAO: RKPC). Hourly meteorological data from 47 observation stations across Jeju Island, collected between 2019 and 2023, were split into training (60%), validation (20%), and test (20%) sets to develop individual prediction models for lead times ranging from 1 to 6 h. A probabilistic prediction model was developed by assigning weights to individual models according to their true skill statistic performance. Validation using an independent 2024 dataset showed that the light gradient boosting machine-based probabilistic model exhibited the highest predictive performance, achieving an area under the receiver operating characteristic curve of 0.883. The Shapley additive explanation analysis identified wind components (U, V) as key variables, contributing over 50%, with the significance of pressure and temperature slightly increasing over long-term prediction times (4–6 h). In addition, spatial analysis revealed that nearby airport stations were more influential for short-term prediction times (1–2 h), whereas Mount Halla and offshore stations north of the airport gained greater influence for medium-to long-term prediction times (3–6 h). The ML-based LLWS prediction model offers high accuracy and interpretability, supporting stepwise warning systems and aiding aviation decision-making.

Short-term load forecasting is fundamental for the effective and reliable operation of power systems. Very accurate forecasting methods often involve complex hybrid approaches that combine statistical, physical, and/or intelligent techniques. In this work, we present an innovative, clear, and effective methodology for short-term hourly peak load forecasting that is both simple and highly accurate. The methodology is based on the load forecast used for electricity market purposes, together with fine-tuning dynamic estimation. As a case study, the methodology was applied and tested in Mexico’s interconnected power system. It was implemented across various regions and at both regional and load-\ zone levels of this interconnected power system and, even under a variety of standard and extreme load conditions, achieved outstanding results.

This study compares Logit, Probit, Extreme Value, and Artificial Neural Network (ANN) models using data from 2012 to 2024 in the Taiwan electronics industry. ANN outperforms traditional models, achieving 98% accuracy in predicting financial distress. Two robust distress signals are identified: Return on Assets (threshold: 7.03%) and Total Asset Growth (threshold: −9.05%). The nonlinear impacts of financial distress on variables are analyzed, with a focus on contextual considerations in decision-making. These findings bring attention to the importance of utilizing advanced techniques like ANN for improved predictive accuracy, offering profound clarification for risk assessment and management.