Machine Learning-Driven Site Selection for Wind Farms in Poland

Energies, vol. 18, pp. 6038

Abstract

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.

Authors 4

1. Artur Amsharuk corresponding

   Bialystok University of Technology

   Affiliation as printed

   Faculty of Civil Engineering and Environmental Sciences, Białystok University of Technology, Wiejska 45E, 15-351 Białystok, Poland

2. Grażyna Łaska

   Bialystok University of Technology

   Affiliation as printed

   Faculty of Civil Engineering and Environmental Sciences, Białystok University of Technology, Wiejska 45E, 15-351 Białystok, Poland

3. Kagan Yüksel Aachen Institute for Future Energy Consumer Needs and Behavior School of Business and Economics / E.ON Energy Research Center

   RWTH Aachen University

   Affiliation as printed

   Institute for Future Energy Consumer Needs and Behavior (FCN), School of Business and Economics/E.ON Energy Research Center, RWTH Aachen University, Mathieustrasse 10, 52074 Aachen, Germany

4. Reinhard Madlener corresponding Aachen Institute for Future Energy Consumer Needs and Behavior School of Business and Economics / E.ON Energy Research Center

   Norwegian University of Science and Technology · RWTH Aachen University

   Affiliation as printed

   Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology (NTNU), Sentralbygg 1, Gløshaugen, 7491 Trondheim, Norway

   Institute for Future Energy Consumer Needs and Behavior (FCN), School of Business and Economics/E.ON Energy Research Center, RWTH Aachen University, Mathieustrasse 10, 52074 Aachen, Germany