In this study, an in-depth analysis is presented on forecasting aggregated wind power production at the regional level, using advanced Machine-Learning (ML) techniques and feature-selection methods. The main problem consists of selecting the wind speed measuring points within a large region, as the wind plant locations are assumed to be unknown. For this purpose, the main cities (province capitals) are considered as possible features and four feature-selection methods are explored: Pearson correlation, Spearman correlation, mutual information, and Chi-squared test with Fisher score. The results demonstrate that proper feature selection significantly improves prediction performance, particularly when dealing with high-dimensional data and regional forecasting challenges. Additionally, the performance of five prominent machine-learning models is analyzed: Long Short-Term Memory (LSTM) networks, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Extreme-Learning Machines (ELMs). Through rigorous testing, LSTM is identified as the most effective model for the case study in northern Italy. This study offers valuable insights into optimizing wind power forecasting models and underscores the importance of feature selection in achieving reliable and accurate predictions.

Accurate forecasting of Photovoltaic (PV) power generation is essential for integrating renewable energy into power grids, ensuring operational stability, and advancing sustainable energy management. This study examines the influence of training data availability, forecast horizons, and ensemble learning techniques on the accuracy of regional PV power forecasting. A sliding window approach is employed to evaluate model performance over an entire year, capturing the effects of seasonal variations and diverse data conditions. The results show that the amount of available training data, the selected forecast horizon, and the specific combination of base models within ensemble methods substantially influence forecasting accuracy. Crucially, the findings reveal that the effectiveness of an ensemble model is shaped by the learning algorithm and the degree to which its base models are well-suited to the characteristics of the input data and the forecasting timeframe. These insights highlight the need for adaptive ensemble configurations tailored to both data context and prediction objectives, providing practical guidance for the development of more reliable and flexible PV forecasting solutions.