Machine Learning-Driven Site Selection for Wind Farms in Poland

The use of Renewable Energy Sources necessitates efficient site selection methods, also for wind farm deployment. This study applies a machine learning (ML) approach to optimize wind turbine siting in Poland, addressing limitations of traditional multi-criteria decision-making (MCDM) methods. A multilayer perceptron model was trained on a dataset of 28 spatio-environmental features, including wind speed, power density, land use, and protected areas. The model was validated using 100,000 randomly selected points and applied to identify suitable locations under real-world constraints. Results indicate that, in Poland, the total number of wind turbines, either as part of wind parks or as single turbines, was 4260 in 2023, and as of the beginning of 2025, stood at 5160, representing a 21% increase. The MLP model demonstrates high performance on both validation and testing subsets, with an accuracy of 91% on the test subset. In this research, after applying the multilayer perceptron (MLP) model and filtering points that obtain a prediction score higher than 0.9, a total of 16,807 valid points were obtained. After applying buffer zones, 3040 validation points are obtained, which are suitable for further location of wind turbines. The majority of validation points for potential new wind turbine construction are located on the Baltic coast, totaling 2355 points. Compared to conventional MCDM methods, MLP reveals hidden spatial patterns and enhances predictive accuracy, and is a valuable tool to expand wind energy capacity in Poland and beyond.