Emag Serwis (Poland)

Renewable energy, particularly photovoltaic (PV) systems, plays a crucial role in sustainable energy development. Its production is largely dependent on external factors, especially weather conditions, making the forecasting of generated energy a significant research challenge. Selecting appropriate features that influence electricity production can enhance forecasting accuracy. This paper evaluates various feature selection methods relevant to energy output, aiming to identify the most effective selection strategy and determine the most influential variables. Three groups of methods were analyzed: correlation-based statistical methods, ensemble-based importance metrics, and univariate significance tests. The results highlight the importance of choosing suitable feature selection algorithms to improve the accuracy of PV energy production forecasting.