Four mosaics of deep, continuum-subtracted, CCD images have been obtained over the extensive Galactic radio continuum shell, W50, which surrounds the

Astronomy & Astrophysics (A&A) is an international journal which publishes papers on all aspects of astronomy and astrophysics

Astronomy & Astrophysics (A&A) is an international journal which publishes papers on all aspects of astronomy and astrophysics

Astronomy & Astrophysics (A&A) is an international journal which publishes papers on all aspects of astronomy and astrophysics

Accurate short-term and medium-term wind speed and wind direction forecasting are crucial for aviation safety, renewable energy management, and environmental planning, particularly in coastal areas with complex terrains. In this study, four machine learning models (Temporal Fusion Transformer (TFT), LightGBM, CatBoost, and a multi-head Convolutional Neural Network (CNN)) were used for multi-horizon (1–24 h) forecasting in Kastelli, Crete, near the new Heraklion International Airport, using a high-resolution multivariate meteorological dataset (2015–2023). For the wind speed forecasting, the best mean absolute error (MAE) values at each horizon are 1 h = 1.89 m/s (LightGBM), 6 h = 3.12 m/s (CatBoost), 12 h = 3.44 m/s (TFT), and 24 h = 3.38 m/s (TFT). For the wind direction forecasting, the best angular MAE values are 1 h = 8.66° (CatBoost), 6 h = 30.71° (CNN), 12 h = 35.29° (TFT), and 24 h = 25.65° (TFT). Overall, the study indicates that different models outperform at different horizons, with the tree-based models being the most effective for short-term forecasts, the convolutional network performing best at intermediate horizons, and the transformer-based architecture offering stronger performance over longer horizons. Compared to recent literature, the proposed framework achieves measurable improvements and confirms the feasibility of deploying ML-based forecasting tools.

This paper introduces and describes the GSSVET (Geothermal and Solar Skills Vocational Education and Training) project, an Erasmus+ funded project. The added value of the project is the development of a training platform that contains three programs that enable learning, assessment and certification and has been tailored to the needs of target users. The training material in all curricula is formed in a way that triggers the trainee to learn and evaluate at any stage the new knowledge he has learnt and the knowledge he should study more. In this way the training process is not boring or tiring and can be adjusted to each trainee needs.