This article presents an approach to locating wind farms based on open-source data, GIS software (QGiS v.3.32 Lima), and multicriteria decision-making methods such as AHP, TOPSIS and Borda. The article aims to check the availability of the most suitable sites for wind farms in Podlaskie Voivodeship, Poland. After the site-selection process, 704 plots were selected after three stages of exclusion (technical–natural, social, and economic). These plots cover a total area of 32.50 km², about 0.16% of the region’s area. The results show that Podlaskie Voivodeship has the most considerable clustering of suitable sites for wind farm construction in three districts, with a total area of 21.53 km². The first district is in the southwestern part of the region, with an area of 14.84 km²; the second district is in the southeastern part of the region, with an area of 5.59 km²; and the third district is in the northern part of the region, with an area of 1.1 km². A selected area of 32.50 km² for wind farms and single turbines could increase the power capacity by 62% with 131.5 MW. The GIS and multicriteria decision analysis (MCDA) methodology is repeatable and can be used for further research in other voivodeships in Poland and other countries. International and Polish investors and renewable energy sources (RESs) developers can use the research results to select new locations for investments that fit their strategy in the local market.

With global shifts towards sustainable energy models, the urgency to address rising fossil fuel prices, military conflicts, and climate change concerns has become evident. The article aims to identify the development of wind energy in Poland. This study introduces an integrated methodology for enhancing renewable energy capacities by selecting new construction sites for onshore wind farms across Poland. The proposed methodology utilises a hybrid model incorporating multiple criteria decision-making methods, such as the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), alongside the semiautomated spatial analysis method using QGiS software (v. 3.32 Lima). The model considers economic, social, and environmental criteria and limitations, offering a comprehensive approach to the decision-making process. It was found that wind farms occupy 460.7 km² in Poland, with a 250 m buffer around each turbine and a total power capacity of 5818 MW. The results show that an additional 7555.91 km² of selected areas, 2.34% of the country’s area, theoretically offer significant opportunities for wind energy development. The spatial analysis identifies potential sites with promising opportunities for domestic and international renewable energy investors. The study’s findings contribute towards achieving national and EU renewable energy targets while offering a replicable framework for informed spatial planning decisions in other regions.

The article presents the most commonly used multi-criteria analysis methods for choosing the optimal location for future wind parks. The article makes a comparison of the criteria and restrictions of localisation and an overview of the main legal constraints and prospects in the development of renewable energy sources (RES). Financial assistance from the EU to accelerate the achievement of the required indicators was described. Moreover, restrictions considering environmental, social and noise factors that affect the life of the local population and the perception of the landscape visually are important. Additionally, it includes an option for developing wind energy in the absence of the necessary space for construction. In a new approach for the location of the wind farm, to the investors and another researcher related to the topic of wind turbine foundations, we indicate the most important aspects of wind energy control that should be taken into account in wind farm location proceedings.

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.

The present work examines the influence of environmental, topographical, and natural factors on the operational parameters of wind farms in Poland. Motivated by the ongoing transition to renewable energy and recent regulatory changes, the analysis utilises a comprehensive dataset of all wind farms in Poland, 2585 wind turbines. In statistical analysis, two grouping parameters were applied—wind farm area (0–5 km², 5–10, 10–15, 15–20 and 25–30 km²) and the power capacity of single turbines (0–1 MW, 1–2, 2–3, 3–4 MW)—to assess spatial and technical variability. A range of non-parametric statistical tests, including the Kruskal–Wallis test and Dunn’s post-hoc test with false discovery rate correction, was used to evaluate differences among subgroups. The results reveal that wind farms of 15–20 km² were farthest from roads (126.7 m), while those of 25–30 km² were the closest (27.3 m). In terms of forest proximity, farms of 10–15 km² were the closest (median: 280.2 m), while those of 5–10 km² were significantly more distant (479.7 m). Wind farms with 3–4 MW turbines were more distant from surface water (744.5 m) than those with 1–2 MW turbines (615.0 m). Surprisingly, larger farms (25–30 km²) were closer to Natura 2000 areas (3494 m) than smaller ones (0–5 km², 5505 m). This indicates a complex interaction of factors influencing the location of wind farms. These findings provide valuable insights for optimising site selection and supporting sustainable wind energy development in Poland, depending on the planned area of investment and wind turbine model.