Innovative GIS and Remote Sensing Approaches for Revealing Hidden Wind Energy Hotspots and Optimizing Wind Farm Siting

Abstract

Ensuring a sustainable and renewable energy supply is a critical challenge for developing nations. This study aims to identify optimal locations for wind power development in the Kurdistan Region (KRG) of Iraq by integrating remote sensing, geographic information systems (GISs), and multicriteria decision-making (MCDM) techniques, including Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The results indicate that ~21% of the study area (8277 km²) demonstrates excellent and good potential for wind power generation, with a total estimated capacity exceeding 48,000 MW. Furthermore, 3332 sites with individual generation capacities of over 3 MW were identified, showcasing substantial opportunities for large-scale wind farm development. The analysis revealed wind speeds ranging from 7 to 14 m/s in the most suitable areas, ensuring optimal energy production. This research introduces a novel framework that integrates advanced spatial analysis with MCDM methods to optimize wind farm siting, considering critical factors such as wind resource assessment, site characteristics, environmental and social impacts, geotechnical constraints, and infrastructure availability. The findings suggest that the KRG has the potential to produce 42.9 TWh of electricity annually, which could save ~5.8 million tons of natural gas and reduce 16 million tons of CO₂ emissions each year. These results highlight the region’s potential to emerge as a regional hub for wind energy, contributing significantly to global efforts in reducing fossil fuel dependency and mitigating climate change. This study provides a robust scientific foundation for policymakers and planners, offering a comprehensive and accurate assessment of wind energy potential. By integrating multiple decision-making models and high-resolution spatial data, this research enhances the reliability and applicability of its findings, serving as a valuable tool for sustainable energy development.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

1. Introduction

Energy is a fundamental driver of economic growth and serves as a key connector among countries globally. The rapid growth of the global economy and population has led to a significant increase in energy demand in recent decades [1]. Currently, ~80% of the world’s electricity is generated from fossil fuels, including coal, oil, and natural gas [2]. However, fossil fuel reserves are finite, and their extensive use disrupts the Earth’s natural balance, contributing to critical issues such as environmental pollution and climate change [3].

Renewable energy sources (RESs) play a vital role in addressing these challenges and shaping the world’s energy future. Energy resources are broadly categorized into fossil fuels, renewable resources, and nuclear energy [4]. RESs, including solar, wind, biomass, and geothermal energy, are replenishable and environmentally friendly alternatives to fossil fuels [5].

The growing demand for energy has driven a transition toward renewable energy, which offers several advantages, including reduced environmental impact and decreased reliance on foreign energy imports [6]. This shift enables countries to produce energy domestically while minimizing ecological harm [7]. Currently, RESs meet ~14% of the global energy demand [8].

Among renewable energy technologies, wind power stands out for its efficiency and cost-effectiveness. As the second most widely deployed RESs, wind energy is essentially a derivative of solar energy. Temperature and pressure differences caused by the uneven heating of the Earth’s surface, oceans, and atmosphere drive wind formation. Wind turbines harness this energy, with their location primarily determined by wind speed. However, factors such as environmental, economic, and social considerations also play a critical role in planning robust wind farms [6, 9–11].

The process of selecting a site for a wind farm is complex, involving technical factors (e.g., wind speed and terrain), environmental concerns (e.g., wildlife impacts), social aspects (e.g., public acceptance), and economic considerations. National regulations further influence decisions for both onshore and offshore wind farms, requiring comprehensive data analysis [12].

The Kurdistan Region (KRG) of Iraq, home to 6,584,335 people, faces a substantial energy demand of ~7000 MW. However, current energy production meets only half of this requirement, supplying up to 3500 MW. This shortfall highlights the urgent need to adopt RESs. This research focuses on utilizing geographic information systems (GISs) and the principles of clean, renewable energy—particularly wind power—to address the energy gap in the KRG. By identifying optimal locations for wind power plants, this study aims to reduce reliance on polluting energy sources, such as diesel generators and fossil fuels [13].

Transitioning to renewable energy will not only enhance energy independence in the KRG but also mitigate the environmental damage associated with traditional energy production. Leveraging its strategic location and natural resources, the region has the potential to lead clean energy initiatives and establish a sustainable energy future. This effort will lay the foundation for an energy mix centered on renewable resources, ensuring environmental preservation for future generations.

2. Literature Review

2.1. Wind Energy

The development of novel and sustainable energy sources—especially wind energy—for electricity generation has attracted significant attention in the late twentieth and early twenty-first centuries. Scientists and researchers have focused on accelerating the determination of design parameters for wind energy generation [14]. As one of the indirect energy sources from the sun, wind is generated by the differential heating of the Earth’s surface, which continuously causes air movements. Wind energy is seen as a flexible, environmentally friendly option that enhances national energy security amid the dwindling supplies of fossil fuels, which threaten the long-term sustainability of the global economy. Specialized design techniques are needed due to the unique technical characteristics and requirements of wind turbine technology [15]. Wind power generation offers advantages over other renewable energy technologies because of its established infrastructure, relative cost-competitiveness, and technological maturity. Future national energy policies are expected to increasingly incorporate wind energy [16, 17]. Compared to other RESs such as solar or tidal energy, wind energy exhibits more variability and diffuse energy flux. To maximize resource utility, optimize design, and ultimately reduce energy production costs, it is essential to understand and define the variability in wind velocity at specific sites considered for wind energy conversion technology (WECT) development [18, 19]. Wind energy is a practical and cost-effective alternative for uses like water pumping and electricity generation. However, the wind resource is not constant in space and time. Wind energy is more susceptible to geographic and weather variations than solar energy. These characteristics make evaluating wind resources a crucial component of wind energy applications [19]. The core components of any wind energy system are wind turbines, which convert wind energy from mechanical to electrical power. These systems, which include wind turbines generating electricity or windmills powering machinery, also require additional components like energy storage units or connections to power distribution networks [20]. In conclusion, the development of wind energy plays a vital role in the global transition to sustainable energy sources, offering a reliable, sustainable, and financially viable alternative to fossil fuels. To fully realize wind energy’s potential and ensure a sustainable energy future, continuous research and innovation in this field are essential.

2.2. GIS for Site Suitability Analysis

The use of GISs to evaluate wind power potential in Markazi province, located in western Iran, incorporates a multicriteria decision-making (MCDM) approach. Wind resource selection is based on factors such as technical, environmental, economic, and geographical considerations. The results suggest that about one-quarter (28%) of the study area is suitable for large-scale wind farms, applying the same standards used globally, indicating potential for wind power plants in western Iran [21]. A study conducted to determine wind power density in Sweden using GIS software considered system performance, site topographical constraints, and ecological limitations. The analysis revealed two types of restrictions to quantify wind potential, suggesting promising prospects for wind farms to contribute to future renewable energy goals in Sweden. This study highlights the importance of wind energy development in Sweden, attracting attention from key stakeholders, particularly policymakers [22]. Another study assessed wind energy potential in Cameroon using GIS and World Climate data. It identified sites, with a preference for the northern regions, covering ~10,344.42 km². The Boolean system in GIS was applied to determine the best locations based on economic, technical, and environmental criteria. The study noted that land cost and military base considerations were excluded from the analysis and emphasized the need for further research to compare methodologies and integrate other RESs [10]. In Thailand, research on wind turbine installation sites focused on highland areas with wind power potential. No-go zones included highlands above 200 m and steep slopes, while urbanized plains and coasts became inaccessible due to infrastructure limitations. Suitable areas, totaling 143,842 ha, are primarily located in Narathiwas, Nakhorn Sri Thammarat, and Phatthalung provinces. Additional suitable regions covering 198,763 ha are found in Nakhon Si Thammarat, Songkhla, and Phatthalung provinces. Moderate suitability zones span 284,806.3 ha, primarily in Songkhla, Nakhon Si Thammarat, and Phatthalung. Exclusion zones were not considered in the analysis [23]. A cost-effective approach for selecting wind turbine locations in Turkey’s inland regions, focusing on Konya province, suggests that with 65 turbines installed in two identified zones, a payback period of 6.37 years could be achieved. This study provides a practical solution for further wind energy development in Turkey [6]. A GIS-based study for siting solar power plants in Malaysia used the analytic hierarchy process (AHP) method and emphasized high-resolution data and population density for better site selection. Weighted overlay tools in GIS helped identify the most suitable locations for power plants, aligning with renewable energy goals [1].

2.3. MCDM for Site Selection

MCDM is a powerful tool for analyzing complex problems involving multiple, often conflicting criteria. These methods are particularly useful in situations where both qualitative and quantitative criteria—such as cost, quality, environmental impact, and social considerations—must be evaluated simultaneously. In renewable energy, MCDM has been increasingly employed to identify optimal locations by considering diverse factors, including natural resources, infrastructure accessibility, economic and social impacts, and technical constraints. Numerous studies have utilized these methods to evaluate and select suitable sites for wind farm development, as reviewed below [24].

A study in Saudi Arabia combined MCDM with GISs) to assess suitable locations for wind farm construction. The findings identified specific regions—such as Ras Tanura, Turaif, and Al-Wajh—as highly suitable for wind energy development due to their favorable wind resources. In contrast, central and southeastern regions of Saudi Arabia were deemed unsuitable due to insufficient wind resources and inadequate infrastructure. These results provide a foundation for strategic decision-making in renewable energy development in the country [25].

In Nigeria, the fuzzy type-2 Analytic HAHP was used to identify the best locations for wind farm development [26]. The study focused on northern regions, highlighting areas such as Bauchi, Jigawa, Kaduna, and Kano as the most suitable for wind energy projects. The analysis revealed ~125,728.6 km² of land as suitable for wind farm installation, with around 2650.1 km² classified as highly suitable [27].

On the Greek island of Lesvos, researchers combined MCDM and GIS to identify optimal sites for wind turbine installation. The study emphasized economic, social, environmental, and technical priorities, demonstrating that sensitivity analysis plays a crucial role in determining the relative importance of criteria during site evaluation [28]. Similarly, another study in Greece applied the AHP method with GIS to evaluate suitable locations for wind energy projects. Focusing on the island of Fournoi Korseon, the research examined six key criteria, including wind speed, elevation, settlements, roads, telecommunication sites, and archaeological sites. Results showed that ~27.9% of the island’s land area is unsuitable for wind turbine installation, and detailed suitability maps were developed through sensitivity analysis [12].

In Tunisia, researchers investigated the potential for wind and solar energy development, identifying social, political, and financial barriers that hinder progress in this sector. The study emphasized that regions with high renewable energy potential should be prioritized for investment to drive local development. Additionally, it proposed GIS-based MCDM as a tool to identify highly suitable areas for renewable energy projects [2].

In Iran, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was applied to evaluate four potential locations for wind farm development: Shahrood, Khorramdarreh, Zabol, and Abadeh. These sites were assessed based on criteria such as wind speed, project safety, social acceptance, distance from the power grid, and infrastructure status. Shahrood emerged as the most suitable location due to its favorable conditions across these criteria [29].

In Istanbul, Turkey, the AHP method was used to determine suitable sites for solar power plants. Criteria weighting was based on expert opinions and survey results, with solar irradiation and sunshine duration identified as the most influential factors. Spatial analysis using GIS was employed to map suitable areas, incorporating land use and availability of vacant land. This study provided essential guidelines for renewable energy development in Istanbul [7].

In summary, this literature review demonstrates that MCDM methods, such as AHP, ANP, ELECTRE, and TOPSIS, are increasingly applied in renewable energy research. These methods effectively evaluate resources, technologies, locations, and projects related to renewable energy. Among them, AHP stands out due to its simplicity and flexibility, making it a popular choice for selecting suitable sites for wind and solar energy projects [30].

Building on these findings, the integration of MCDM with GIS tools offers a comprehensive and efficient approach to addressing complex challenges in renewable energy development. This combined approach enables precise evaluation of multiple criteria and assists decision-makers in selecting optimal options by considering all relevant factors.

3. Materials and Methods

3.1. Study Area

The KRG, located in northern Iraq, is a semiautonomous area with significant potential for renewable energy development, although this field remains relatively underexplored. This study leverages the region’s climatic conditions, including temperature, sunshine duration, wind speed, rainfall rates, and solar radiation, as key indicators for assessing various RESs. Given its geographical location, the KRG has access to diverse renewable energy resources such as hydropower, wind, solar, biomass, and geothermal energy [31].

The KRG spans ~46,862 km² and shares borders with Iran to the east, Turkey to the north, and Syria to the west. It encompasses four governorates: Erbil, Sulaymaniyah, Duhok, and Halabja, with a total population of 6,584,335 as of 2020. The region is characterized by diverse topography, including high mountains and mountain ranges in the north and northeast along the borders with Turkey and Iran. In contrast, the central and southern parts primarily consist of rolling hills and fertile plains [31].

This geographical diversity significantly influences various aspects of life in the region, including infrastructure development, agricultural practices, and environmental conservation. These unique features make the KRG an important and intriguing area for research on renewable energy potential and sustainability [31]. Figure 1 illustrates the location and boundaries of the KRG, including its governorates, international borders, and population distribution.

[figure(s) omitted; refer to PDF]

3.2. Electricity Generation and Challenges in the Kurdistan Regional Government

Electricity generation in the KRG relies primarily on a combination of natural gas and hydroelectric power plants. Natural gas is the dominant fuel source for most of the region’s power plants, though frequent fuel shortages often limit its availability. These shortages significantly challenge the reliability and consistency of electricity supply, impacting both residential consumers and the industrial sector [31–33].

Hydropower plays a vital role in the region’s energy mix, with key contributions from dams such as Dukan and Darbandikhan. The Dukan Dam, constructed between 1954 and 1959, is 1,180,360 m long and 116.5 m high, with a power station capable of generating up to 400 MW. Similarly, the Darbandikhan Dam’s hydroelectric station has an installed capacity of 249 MW. However, both of these sources are vulnerable to fluctuations in water levels, which can severely affect the overall electricity generation capacity of the region [31–33].

The KRG faces a daily maximum electricity demand ranging from 6500 to 7000 MW. Although the region’s installed generation capacity exceeds 6700 MW, the actual output is significantly lower. Currently, the region’s 14 power plants generate only between 3500 and 4000 MW, with ~1500 MW produced in Erbil, Sulaymaniyah, and Duhok, each managed by the Ministry of Electricity (MOEL). The gap between electricity demand and supply is driven by factors such as fuel shortages and infrastructure limitations [31–33].

A significant challenge in the electricity sector is the scarcity of natural gas, which is essential for power generation. For example, power plants such as Ahmad Ismail and Kormor (supplied by Dana Gas) are unable to operate at full capacity due to insufficient gas supplies. The region’s dependence on natural gas exacerbates this issue [31–33].

Kurdistan’s power stations consist of dual-fuel gas turbines, capable of operating on either gas or diesel. Gas is sourced from fields like Khor Mor and Khurmala. To address the shortfall between electricity demand and generation capacity, diesel generators are employed to provide supplementary power, particularly during peak demand periods or when plants are offline due to fuel shortages or maintenance, as noted by the Ministry of Natural Resources [31–33].

In summary, the electricity sector in Kurdistan faces significant challenges, including fuel scarcity and insufficient infrastructure. Although natural gas and hydropower are the primary energy sources, their effectiveness is hindered by factors such as fluctuating water levels and inadequate infrastructure. Addressing these challenges is crucial for ensuring a stable and reliable electricity supply for both residents and industries in the region. The Kurdistan Regional Government has prioritized improving the electrical grid, as emphasized by the MOEL.

Given the existing challenges in electricity generation and the increasing demand for reliable power in the KRG, identifying suitable locations for wind farms is essential to enhance the region’s energy security and sustainability. Wind energy offers a promising alternative to reduce reliance on natural gas and hydropower and can provide a more consistent and sustainable energy source for the growing demands of the region.

3.3. Identification and Interpretation of Spatial Criteria for Wind Power Plant Site Selection

In this section, drawing on insights from previous studies as well as our own research, we identify 13 subcriteria that are crucial for the site selection of wind power plants. These subcriteria are classified into five main categories: (1) wind resource assessment, which includes wind speed (m/s); (2) site characteristics, consisting of slope (%) and altitude (m); (3) environmental and social Factors, involving distance from villages (m), distance from towns (m), and land use; (4) geotechnical considerations, including distance from fault lines (m), earthquake magnitude (mag), and distance from rivers and floodplains (m); and (5) infrastructure considerations, encompassing distance to power lines (m), distance to power substations (m), distance from airports (m), and distance from roads (m). Each of these subcriteria will be discussed in detail in the following sections.

3.3.1. Wind Speed

The wind speed is a crucial factor in wind energy generation and must be carefully considered when selecting a site for wind power plants. For this study, wind speed data were obtained from the Global Wind Atlas (GWA), which serves as a key tool for decision-making by policymakers, planners, and investors involved in the development of wind power resources globally (GWA, n.d.). Wind speed directly determines the amount of power generated by turbines. However, wind turbines have specific limits for minimum and maximum wind speeds for effective electricity generation, as defined by the manufacturers. Generally, turbines operate most efficiently within a wind speed range of 3–25 m/s [9].

Wind speed distribution varies globally due to factors such as local climate, topography, and surface conditions. The suitability of a wind farm site largely depends on the wind speeds in the area. Turbines are most effective when placed in locations where the wind speed is within an optimal range, as wind speed is the primary factor in site selection. Numerous studies have highlighted that the average interpolated wind speed is a fundamental criterion for identifying favorable sites for wind farms [2].

It is particularly relevant to assess wind speeds at the hub height of modern turbines, as this provides more accurate information for selecting potential wind power plant locations. Wind speed calculations should also consider the surface roughness of the area, which can affect wind flow at different heights. In Equation (1), V₁ and V₂ represent the wind speeds at heights Z₁ and Z₂, respectively, and n is the value of the wind shear coefficient. This coefficient, which depends on local topography, is often assumed to be 1/7 for open land [9]:$$\begin{array}{}\text{(1)}& V=V_1{\frac{Z_2}{Z_1}}^N.\end{array}$$

3.3.2. Proximity to Power Infrastructure (Power Lines and Substations)

Proximity to power infrastructure, including transmission lines and substations, is a critical factor in the site selection process for wind farms. Locating wind farms close to the power grid helps minimize the costs associated with long distribution lines, which can lead to significant expenses such as cable installation and energy losses. Additionally, being near the grid reduces connection costs and helps mitigate transmission losses. The accessibility and availability of the electricity network are essential considerations, as they directly impact the feasibility and economic viability of wind energy projects. By strategically locating wind farms near existing power lines and substations, both construction and operational costs can be significantly reduced, addressing a key challenge in the wind energy industry [10].

3.3.3. Proximity to Settlements (Villages and Towns)

Determining the optimal distance between wind farms and residential areas is critical for several reasons. First, it minimizes noise pollution and other disturbances that could negatively affect nearby communities. Second, it ensures that wind speeds remain adequate for efficient energy generation. Third, it provides sufficient space for future urban development without interfering with the wind farm’s operations. Proximity to towns and villages can also reduce energy transmission costs and facilitate easier energy distribution. However, it is important to strike a balance to avoid potential conflicts with future urban expansion. Therefore, selecting the appropriate distance from settlements is a key factor in wind farm siting decisions [25].

3.3.4. Proximity to Transportation Networks (Roads and Highways)

Selecting sites close to transportation networks, including roads and highways, is crucial for minimizing costs during both the construction and operational phases of wind farms. Proximity to these networks reduces the expenses related to transporting equipment, materials, and personnel. Generally, sites located far from roads are less favorable for wind farm development due to the increased logistical challenges. While there is no fixed minimum distance required between wind turbines and roads, strategically utilizing existing road networks can significantly lower transportation costs and improve the feasibility of the project [10, 25].

3.3.5. Safe Distance From Airports: Mitigating Radar Interference

Wind turbines can cause interference with aviation radar systems, necessitating a significant buffer zone around airports. Aircraft tracking relies on the frequency of return signals (Doppler effect), and the rotation of turbine blades can disrupt the performance of air traffic control radar. Although wind turbines are typically located away from flight paths to minimize the Doppler effect, insufficient distance between wind farms and airports can still pose risks to air traffic control operations. Therefore, maintaining a safe distance between wind turbines and airports is critical to ensuring aviation safety and preventing radar interference [25].

3.3.6. Impact of Terrain Slope on Wind Farm Siting

The slope of a region is a critical factor influencing the effectiveness and installation of wind turbines. Obstructions such as hills or mountains can hinder access to wind energy, reducing turbine efficiency. Additionally, as the slope increases, the costs associated with turbine installation and maintenance also rise, making affordability a key consideration. Steep terrain poses significant challenges for accessibility and maintenance, further driving up costs. For economic viability, it is essential to prioritize flat or gently sloping terrain for wind farm siting, as steep slopes lead to higher construction and maintenance expenses. Therefore, selecting sites with lower slopes is more suitable for establishing cost-effective and accessible wind farms [25].

3.3.7. Impact of Altitude on Wind Farm Siting

When selecting sites for wind power plants, altitude (elevation above sea level) is a critical factor to consider. Higher altitudes can complicate the transportation of equipment and increase construction and maintenance costs, making such locations less economically viable. Additionally, extreme altitudes may pose challenges to operational efficiency and accessibility. Therefore, to maintain cost-effectiveness and ensure smooth operations, it is advisable to avoid excessively high altitudes when siting wind farms. Prioritizing lower or moderate elevations can help optimize both economic and operational outcomes [9].

3.3.8. Proximity to Earthquake Zones and Fault Lines

Earthquakes can have devastating effects on power plants, posing life-threatening risks to personnel during both construction and operation. The destructive impact of earthquakes is most severe near their epicenters, making it essential to locate wind farms as far as possible from earthquake-prone areas [9]. Similarly, maintaining a safe distance from geological fault lines is critical to ensuring the structural integrity and safety of wind power facilities. By selecting sites away from earthquake zones and fault lines, developers can minimize risks to infrastructure and personnel, thus enhancing the overall safety, stability, and long-term viability of wind farms [9].

3.3.9. Distance From Water Bodies and Flood-Prone Areas

Preserving the natural environment requires establishing protective perimeters around aqueducts and other water bodies. Furthermore, the dynamic nature of rivers and the associated risk of flooding make it essential to locate wind power plants at a safe distance from surface water sources. By situating wind farms away from riverbanks and flood-prone areas, their operation can remain uninterrupted, ensuring both safety and security. This approach not only helps protect the environment but also maintains the integrity of the wind energy system by safeguarding it from potential hazards such as flooding and erosion [9].

3.3.10. Land Use Considerations for Wind Farm Siting

Selecting appropriate sites for wind power stations requires careful consideration of land use to avoid substantial conflict with sensitive areas such as forests, nature reserves, archaeological sites, and military zones. These areas are generally unsuitable for wind power infrastructure due to environmental, cultural, or security concerns. Additionally, regions like forests, lakes, and urban areas lack the potential for wind energy development and should be avoided. To maximize both economic and environmental benefits, it is essential to prioritize land use that minimizes ecological disruption while optimizing wind energy generation. This approach ensures that wind farms are developed in a way that balances cost-effectiveness with environmental preservation [2, 9].

3.4. Methodology

3.4.1. Data Collection and Preparation

In this study, data were collected from multiple sources, including the GWA, digital elevation models (DEMs), satellite imagery, and various governmental and institutional repositories. These datasets were processed and prepared for spatial analysis using GIS) tools. The mean wind speed data at a height of 100 m above ground level, averaged over a 10-year period, were sourced from the GWA. To create the DEM, a DEM with a spatial resolution of 28 m by 28 m was obtained from the United States Geological Survey (USGS). Using this DEM, a slope map was generated as one of the subcriteria for the analysis.

For mapping the power transmission lines and substations within the study area, hardcopy maps were acquired from the MOEL of the KRG and other relevant administrative offices. These maps were georeferenced and digitized within a GIS environment to generate vector layers representing the power infrastructure. Additionally, maps detailing the locations of villages, cities, roads, airports, and surface water resources (including dams) were obtained in hardcopy format from the provincial administrations of the KRG. These maps were also georeferenced and digitized to produce spatial layers for further analysis.

Data regarding fault lines and historical earthquake events (occurring within the past 100 years) were obtained from the USGS Earthquake Hazards Program. These datasets were converted into GIS-compatible formats, and fault lines were further extracted from geological maps of Iraq. The river network and drainage system were derived using the DEM and hydrological tools available in QGIS software, which facilitated the extraction of river pathways and drainage patterns across the study area. For land use and land cover (LULC) mapping, Sentinel-2 satellite imagery was employed. Images covering the study area for the spring and summer seasons of 2024 were downloaded and preprocessed, and supervised classification techniques were applied to categorize the imagery into distinct land use types. All datasets were integrated into a unified GIS framework, enabling comprehensive spatial analysis and modeling for the study.

3.4.2. Preparation of Subcriteria Maps

After data collection, the identified subcriteria were processed and converted into spatial layers using GIS tools. Each criterion, including wind speed, slope, altitude, distance to infrastructure, and environmental factors, was individually mapped. For distance-based criteria—such as proximity to fault lines, villages, towns, rivers, floodplains, power lines, power substations, airports, and roads—the Euclidean distance tool was utilized to generate spatial layers. Land use maps were produced using the supervised maximum likelihood classification method. A slope map was derived from the DEM using the slope tool in QGIS.

To create the earthquake magnitude map, historical seismic data (including the location and magnitude of earthquakes over the past 100 years) were interpolated using the inverse distance weighting (IDW) method, a geostatistical approach. Wind speed data at 100 m above ground level, sourced from the GWA, were processed to develop a wind speed map. These standardized spatial layers served as the foundation for subsequent analysis and decision-making processes.

3.4.3. GIS-Based Spatial Analysis

3.4.3.1. Euclidean Distance Analysis

Euclidean distance analysis was utilized in this study to measure spatial attributes critical for wind power plant site selection. This method calculates the straight-line distance between two points and was applied to assess proximity-based criteria, such as distances to infrastructure (e.g., power lines, roads, and airports), settlements, and fault lines. Spatial data, including locations of villages, cities, faults, rivers, power lines, roads, and airports, were processed using GIS tools to generate distance layers [34, 35].

Although Euclidean distance is effective for measuring straight-line distances, it does not consider physical barriers, which may reduce its accuracy in certain contexts. To mitigate this limitation, field validation and stakeholder input were incorporated to refine the analysis and ensure the results align with real-world conditions [34].

3.4.3.2. Satellite Image Processing

Satellite image processing involves transforming raw satellite imagery into usable data to extract relevant information about the Earth’s surface. Key procedures include distortion correction, image quality enhancement, and categorization of land features such as vegetation, water bodies, and urban areas. The processed data are valuable for applications like urban planning, environmental monitoring, and identifying optimal locations for wind farms [36].

In this study, GIS was employed alongside satellite image processing to assess site characteristics for wind farm power plants. Multitemporal and multispectral satellite images were utilized to analyze changes in LULC, a critical factor in determining suitable areas for wind farm development. Techniques such as image segmentation—partitioning images into regions with similar characteristics to enhance areas of interest and reduce noise—resampling, geometric correction, and cloud removal were applied to improve image quality and accuracy [36–38].

3.4.3.3. IDW Analysis

The IDW method is a commonly used technique in spatial analysis that estimates values at unsampled locations based on the information from nearby points. In this method, the weight of each point is inversely proportional to its distance from the target location, with closer points having a greater influence on the calculation. Due to its simplicity and high efficiency, IDW is widely applied in various practical applications, such as estimating environmental and geospatial data. In this study, we used the IDW method to create a map of earthquake magnitude (mag), estimating the earthquake magnitude at unsampled locations and generating a continuous map of earthquake intensity in the region of interest [39–42].

3.4.4. Wind Energy Resource Assessment and Optimization

Wind energy resource assessment and optimization are critical steps in the planning and development of wind farms, focusing on two interrelated aspects essential for maximizing the efficiency and sustainability of wind energy projects. The first aspect, power density analysis, involves quantifying the wind energy potential in a given area by considering factors such as wind speed, air density, and topographic conditions [43, 44]. The second aspect, the configuration and placement of wind turbines, addresses the optimal arrangement of turbines to minimize turbulence effects, maximize energy output, and ensure efficient land use [43, 44]. GIS tools and spatial analysis techniques play a pivotal role in both aspects by integrating various environmental and geographical variables. GIS-based power density maps assist in identifying high-potential zones for wind turbine installation, while advanced spatial modeling helps optimize turbine placement to balance energy efficiency with environmental considerations. The following subsections provide a detailed discussion of these topics, with a focus on their practical applications and relevance to sustainable energy planning.

3.4.4.1. Power Density Analysis

GIS has evolved into a key tool for power density analysis in renewable energy applications, particularly in wind energy resource assessment and optimization. Power density, defined as the ratio between the amount of power generated and the unit area, is a critical metric in renewable energy planning. The power density (P_d) for a wind power plant can be calculated using Equation (2) [45]:$$\begin{array}{}\text{(2)}& P_d=\frac{1}{2}\rho v^3,\end{array}$$where P_d is the power density (W/m²), ρ is the air density (kg/m³), and υ is the wind speed (m/s).

GIS enables the integration of various geographical variables—such as wind speed, wind direction, topographic features, and existing land use—to generate actionable power density maps [45]. These maps are instrumental in identifying locations with the highest potential for wind turbine installation, thereby maximizing energy output while minimizing land use and environmental impact [46]. In this study, power density maps at a height of 100 m were obtained directly from the GWA. To estimate the wind energy potential and the power generated by each wind turbine, Equation (3) was used [47]:$$\begin{array}{}\text{(3)}& P=P_d\cdot C_P\cdot A,\end{array}$$where P is the power generated by a turbine (W), P_d is the power density (W/m²), A is the swept area of the wind turbine blades (m²), and C_p is the power coefficient (generally considered as 0.40).

Furthermore, GIS facilitates the analysis of power density under varying conditions, such as different turbine heights or types, enabling a comparative evaluation of potential energy yields [48]. This capability is particularly valuable for policymakers and energy planners, as it supports data-driven decision-making to achieve renewable energy goals effectively and sustainably [49]. Beyond site selection, GIS-based power density assessments are also employed in the strategic planning of transmission infrastructure, influencing the design and routing of power lines based on the spatial distribution of energy generation [50]. By incorporating power density variables, GIS contributes to the development of more sustainable and spatially efficient renewable energy systems [51].

3.4.4.2. Optimal Configuration and Placement of Wind Turbines

The configuration and placement of wind turbines are critical factors in the design and efficiency of wind farms. Substantial attention must be given to the number of turbines and their spatial arrangement, particularly in offshore zones and demonstration projects. One of the key factors influencing turbine performance is the spacing between turbines. If turbines are placed too close together, the airflow from one turbine can cause turbulence, reducing the efficiency of downstream turbines. Conversely, if turbines are spaced too far apart, land use becomes inefficient, limiting the number of installations and overall power generation [52, 53].

Research indicates that the optimal spacing between turbines should be between three and five times the rotor diameter (D) within a row and between five and nine times the rotor diameter between rows, as illustrated in Figure 2 [52, 53]. In this study, a turbine rotor diameter of 120 m was considered, based on previous research [47]. A spacing of 5D was adopted for both row and column arrangements to maximize the number of turbines, enhance efficiency, and optimize power generation (Figure 2). For further details on the decision-making process, site selection criteria, and technical parameters, refer to Appendices A, B, and C.

[figure(s) omitted; refer to PDF]

3.4.5. MCDM Framework

In this study, a GIS-based MCDM framework was employed to systematically evaluate and rank potential sites for wind farm development. Each decision criterion—such as wind speed, slope, altitude, and proximity to infrastructure—was represented as a thematic map layer [12]. These layers were integrated using spatial analysis techniques, including overlay operations, to generate suitability maps that delineate areas with varying levels of suitability for wind energy projects. The relative importance of each criterion was determined through expert knowledge and stakeholder input, ensuring that the decision-making process accounted for both technical and socioenvironmental considerations [25].

The MCDM framework utilized in this study incorporated multiple decision-making techniques to evaluate and rank potential sites. The AHP was used to structure the decision problem hierarchically and derive criterion weights through pairwise comparisons, enabling the prioritization of factors based on their relative importance [54]. Additionally, the TOPSIS was applied to assess alternatives by calculating their proximity to an ideal solution, making it particularly effective for handling multidimensional decision problems [55]. Furthermore, the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method was employed to rank alternatives by balancing “maximum group utility” and “minimum individual regret,” thereby providing a compromise solution that addresses conflicting criteria [56].

By integrating these MCDM techniques with GIS-based spatial analysis, the framework offered a robust and comprehensive approach to identifying optimal locations for wind energy projects. This approach ensured the consideration of environmental, social, and economic factors, thereby enhancing the reliability and applicability of the results.

3.4.5.1. Determining Criteria Weights Using the AHP

The AHP is a widely used method for MCDM that employs a hierarchical structure to define complex decision problems and prioritize potential solutions based on user evaluations [57]. AHP relies on pairwise comparisons to determine the relative importance of criteria. The process begins by organizing the decision problem hierarchically: the primary objective is placed at the top level, followed by relevant criteria at the second level, and the alternatives for evaluation at the third level [58].

The next step involves comparing the criteria to rank the alternatives. Decision-makers provide their preferences for the relative importance of each criterion using a verbal scale. These preferences are captured in a reciprocal ratio matrix, where judgments are expressed on a 9-point scale. The weights of the criteria are then calculated by normalizing the eigenvector corresponding to the highest eigenvalue of the reciprocal matrix [25].

In Step 1, pairwise comparison matrices are constructed, where experts assign scores to indicate the relative importance of criteria and subcriteria. Each element ($x_{ij}$) of the matrix K (of size n × n) represents the importance of criterion i relative to criterion j, based on a scale ranging from 1 to 9 [2]. The general form of the matrix is expressed as follows:$$\begin{array}{}\text{(4)}& K=\begin{array}{cccc}{x}_{11}& x_{12}& \cdots & x_{1n}\\ x_{21}& x_{22}& \cdots & x_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ x_{n1}& x_{m2}& \cdots & x_{nn}\end{array},\end{array}$$where i, j = 1, 2,…,n.

To compute the relative weights, the pairwise comparison matrix is first normalized by summing all entries in each column. Each matrix element is then divided by its column total. Finally, the relative weight of each criterion is calculated by averaging the normalized values in each row. After determining the weights, the eigenvector, maximum eigenvalue (λ_max), and consistency index (Ci) are computed using the following equation [2]:$$\begin{array}{}\text{(5)}& Ci=\frac{{\lambda }_{\max -n}}{n-1},\end{array}$$where ${\lambda }_{\max }$ (Equation (4)) represents the maximum eigenvalue for each matrix and n is the number of criteria. In Step 3, finally, the consistency ratio (CR) is calculated to ensure the consistency of the pairwise comparison matrix ($K$) using Equation (6), where RI represents the random index, a predetermined value based on the size of the matrix. If the CR value is 0.10 or less, the pairwise comparisons are deemed consistent. However, if CR exceeds 0.10, significant inconsistencies are present, necessitating a review and possible repetition of the pairwise comparison process [2]:$$\begin{array}{}\text{(6)}& \text{CR}=\frac{Ci}{\text{RI}}.\end{array}$$

3.4.5.2. Site Suitability Assessment via the TOPSIS Method

The TOPSIS approach, developed by Hwang and Yoon, is a widely used method in MCDM problems. It utilizes the Euclidean distance to rank alternatives based on their closeness to the positive ideal solution (PIS) and their distance from the negative ideal solution (NIS). This straightforward and effective method is implemented in seven distinct steps, as outlined in the following. In this study, these steps were carried out using an extensive review of the literature, expert judgment, and practical field knowledge [59]. The steps are manifested as follows:

Step 1: Create the decision matrix $D=d_{ij}$.

Step 2: Standardize the decision matrix. To calculate the $r_{ij}$ in this case, vector normalization is used:$$\begin{array}{}\text{(7)}& r_{ij}=\frac{d_{ij}}{\sqrt{\sum _{i=1}^m{d}_{ij}^2}},i=1,\dots ,m;j=1,\dots n,\end{array}$$

Step 3: Multiply the characteristics weight assigned to each category to create a weighted standardized judgment matrix:$$\begin{array}{}\text{(8)}& v_{ij}=w_j\times r_{ij},i=1,\dots ,m;j=1,\dots ,n,\end{array}$$where the weight of the jth qualities is indicated by $w_j$.

Step 4: Based on the weighted normalized values, find the PIS $A^{\ast }$ and NIS $A^{-}$:$$\begin{array}{}\text{(9)}& A^{\ast }=\begin{array}{cccc}{v}_1^{\ast },& v_2^{\ast },\dots ,v_j^{\ast }& ,\dots ,& v_n^{\ast }\end{array},\end{array}$$$$\begin{array}{}\text{(10)}& A^{\ast }=\begin{array}{cc}\underset{i}{\max }& v_{ij}\mid j\in \end{array},\underset{i}{\min }{v}_{ij}\mid j\in J_2\mid i=1,\dots ,m,\end{array}$$$$\begin{array}{}\text{(11)}& A^{-}=v_1^{-},v_2^{-},\dots ,v_j^{-},\dots ,v_n^{-},\end{array}$$where a set of cost characteristics is represented by $J_2$ and a set of benefit attributes by $J_1$.

Step 5: Determine the distance between the positive ideal value $S^{+}$ and the negative ideal value $S^{-}$. The n-dimensional Euclidean distance is used to calculate the distance between the options:$$\begin{array}{}\text{(12)}& S^{+}=\sqrt{\sum _{j=1}^n{{\nu }_{ij}-{\nu }_j^{\ast }}^2},i=1,\dots ,m,\end{array}$$$$\begin{array}{}\text{(13)}& S^{-}=\sqrt{\sum _{j=1}^n{{\nu }_{ij}-{\nu }_j^{-}}^2},i=1,\dots ,m.\end{array}$$

Step 6: Determine how near the actual solution is to the ideal one $C_i$:$$\begin{array}{}\text{(14)}& C_i=\frac{S^{+}}{S^{+}+S^{+}},0<C_{i\ast }<1,i=1,\dots ,m.\end{array}$$

Step 7: By sorting in descending order, the relative distance $C_i$ may be rated to obtain the final ranking of the options.

3.4.5.3. Site Suitability Assessment via the VIKOR Method

The VIKOR method is a MCDM technique designed to rank alternatives by comparing them to ideal and anti-ideal solutions. It is particularly useful for identifying a compromise solution that balances conflicting criteria, making it well-suited for site selection of wind turbines, where factors such as wind speed, environmental impact, and proximity to infrastructure must be considered. The method was first proposed by Opricovic and Duckstein in 1980, and later refined by Opricovic and Tzeng in 2004 [60]. The system uses a multiranking index to determine which criteria are closest to the ideal [14]. Weights are calculated by comparing the ideal closeness values to the ideal alternatives. The VIKOR technique uses alternatives, that is, $A^1,A^2,\dots .A^m$, and criteria, that is, $C_1,C_2\dots .C_n$, to make decisions. $x_{ij}$ indicates the value of the $A^i$ options, whereas $W_j$ represents the weight based on the $C_j$ criterion. The variables $m$ and $n$ represent the number of choices and criteria, respectively. This technique evaluates two categories of criteria, focusing on bigger and smaller values that indicate greater performance (Equations (15) and (16)):$$\begin{array}{}\text{(15)}& f_j^{\ast }=\underset{j}{\max }{f}_{ij};f_j^{-‘}=\underset{j}{\min }{f}_{ij},\end{array}$$$$\begin{array}{}\text{(16)}& f_j^{\ast }=\underset{j}{\min }{f}_{ij};f_j^{-}=\underset{j}{\max }{f}_{ij}.\end{array}$$

To calculate the final values of $Sj$, $Rj$, and $Qj$, refer to Equations (17)–(19):$$\begin{array}{}\text{(17)}& Sj=\frac{\sum _{j=1}^n{w}_j{f}_j^{\ast }-f_{ij}}{f_j^{\ast }-f_j^{-}},\end{array}$$$$\begin{array}{}\text{(18)}& Rj={\max }_j\frac{w_j{f}_j^{\ast }-f_{ij}}{f_j^{\ast }-f_j^{-}},\end{array}$$$$\begin{array}{}\text{(19)}& Qj=\frac{\nu Sj-S^{\ast }}{S^{-}-S^{\ast }}+\frac{1-\nu R_i-R^{\ast }}{R^{-}-R^{\ast }}.\end{array}$$

4. Result and Discussion

The results and findings of this study are analyzed in three major sections. First, the focus is on creating maps for each criterion and their subcriteria, classifying each one, and calculating their relative potential on the location selection of the wind farms. In the second section, the relative importance of each criterion is determined by using the Analytical Hierarchy Process (AHP. In the third and last section, the models for the potential of wind power plant areas are created using various MCDM models, including the weighted linear combination (WLC) method, the VIKOR method, and the TOPSIS method. At the end of the results is the analyzation of the models and calculation of the potential electricity generation from the wind power plants in the examined area.

4.1. Mapping and Classification of Key Criteria for Wind Farm Suitability

In the first section, we focus on creating maps for the criteria and subcriteria, classifying each one, and calculating the relative potential of each subcriterion class for the wind farm.

4.1.1. Wind Speed Classification and Its Impact on Electricity Generation Potential

The main factor in the selection of suitable areas for wind power plants is the area’s annual wind speed. L, at this height, the wind turbine can economically generate electricity. The minimum wind speed for wind turbine operation is usually around 3.5 m/s, and a maximum wind speed safety stop is around 25 m/s. For optimized electricity production and economical operation, the wind speed should be between 7 and 9 m/s, allowing the turbine to operate at maximum efficiency and generate more electricity. Furthermore, it is very critical during site selection studies to analyze local related data sources about this matter too, because yearly average hourly mean effective wind power (in m/s) values must be at least expected as being between these limits, respectively; for reinforced exploitation, they need to be preferably within the limit.

4.1.1.1. Classifying Wind Speed in the Study Area: A Weighted Analysis for Wind Power Potential

To classify the wind speed in the study area, the wind speed map at 100 m above ground level was divided into six classes from “lowest” to “excellent” based on the annual average wind speed. Considering the wind speed range in the study area, which is from 0.84 to 14.338 m/s, the classification is as follows: (1) lowest, 0.84–3.18 m/s; (2) low, 3.19–5.52 m/s; (3) moderate, 5.53–7.86 m/s; (4) good, 7.87–10.20 m/s; (5) very good, 10.21–12.57 m/s; and (6) excellent, 12.58–14.338 m/s. This classification helps evaluate different points in the area based on the potential for wind power generation and select the best location for establishing a wind power plant. The wind speed classification map at 100 m above ground level is shown in Figure 3. To assign weights to the wind speed classes using the AHP method and considering the area distribution of each class, the following steps were taken:

[figure(s) omitted; refer to PDF]

1. Lowest (0.84–3.18 m/s): This class has the least potential for electricity generation and thus should have the lowest weight.

2. Low (3.19–5.52 m/s): This class has relatively low potential but is higher than the first class.

3. Moderate (5.53–7.86 m/s): This class has moderate potential and should have a higher weight than the previous two classes.

4. Good (7.87–10.20 m/s): This class has good potential but covers a smaller area, hence a moderate weight.

5. Very Good (10.21–12.57 m/s): This class has high potential, but due to the small area, its weight is slightly more than the previous class.

6. Excellent (12.58–14.338 m/s): This class has the highest potential and should have the highest weight, although its area is small.

Considering the area distribution and electricity generation potential, the weights were calculated and are presented in Table 1.

Table 1

Criteria and weighting for optimal wind energy site selection.

Criteria	Subcriteria	Options	Class weighting	Criteria weights
Wind resource assessment	Wind speed (m/s)	0.84–3.18	0.05	0.46
		3.19–5.52	0.10
		5.53–7.86	0.209
		7.87–10.20	0.212
		10.21–12.57	0.214
		12.58–14.338	0.215
Site characteristics	Slope (%)Subcriteria weights (0.45)	0–10	0.31	0.153
		10–20	0.21
		20–30	0.17
		30–40	0.14
		40–50	0.1
		>50	0.07
	Altitude (m)Subcriteria weights (0.55)	101–453	0.35
		453–754	0.25
		754–1098	0.15
		1098–1513	0.11
		1513–2102	0.09
		2102–3609	0.05
Environmental and social factors	Distance from villages (m)Subcriteria weights (0.182)	0–1000	0.04	0.153
		1000–3000	0.2
		3000–5000	0.3
		5000–7000	0.25
		7000–8500	0.15
		8500–9735	0.06
	Distance from towns (m) Subcriteria weights (0.330)	0–5228	0.04
		5228–7868	0.20
		7868–13,096	0.30
		13,096–23,453	0.25
		23,453–43,970	0.15
		4370–84,613	0.06
	Land useSubcriteria weights (0.488)	Bare ground	0.30
		Rangeland	0.25
		Crops	0.20
		Flooded vegetation	0.10
		Trees	0.08
		Snow	0.07
		Water	0
		Built area	0

4.1.2. Assessing the Influence of Proximity to Power Lines on Wind Farm Suitability

In wind power plant siting, the proximity to power lines is a crucial factor. The distance to power lines significantly impacts the cost and feasibility of transmitting generated electricity to the grid. Shorter distances reduce the costs associated with transmission infrastructure and energy losses, making locations closer to power lines more desirable for wind power plant installation. To evaluate this criterion, a distance map from power lines was created, ranging from a minimum distance of 0 m to a maximum distance of 65,045 m. Based on existing standards and previous studies, the distance map was classified into six categories, with each category assigned a specific weight to reflect its impact on wind power plant siting. The classification is as follows: very close (0–1000 m) with a weight of 0.28, indicating very close proximity to power lines, minimal transmission costs, and high suitability for wind power plant siting; close (1001–5000 m) with a weight of 0.22, indicating close proximity, low transmission costs, and suitability for wind power plants; moderately close (5001–10,000 m) with a weight of 0.20, indicating moderate distance, moderate transmission costs, and viability for wind power plant siting; moderately far (10,001–20,000 m) with a weight of 0.14, indicating further distance, higher transmission costs, and less suitability but possible with careful consideration; far (20,001–40,000 m) with a weight of 0.10, indicating significant transmission costs and less desirability for wind power plants; and very far (greater than 40,000 m) with a weight of 0.06, indicating the highest transmission costs and the least suitability for wind power plant siting (Figure 3, Table 2). This classification ensures that areas with shorter distances to power lines are prioritized for the installation of wind power plants, thereby reducing costs and improving the feasibility of energy transmission. The detailed classification and weighting facilitate an efficient and strategic approach to selecting optimal sites for wind power plant development.

Table 2

(Continuation) criteria and weighting for optimal wind energy site selection.

Criteria	Subcriteria	Options	Class weighting	Criteria weights
Geotechnical considerations	Distance from fault (m)Subcriteria weights (0.4313)	0–2000	0.05	0.102
		2000–5000	0.1
		5000–10,000	0.2
		10,000–20,000	0.25
		20,000–30,000	0.3
		>30,000	0.35
	Earthquake magnitude (mag)Subcriteria weights (0.2997)	3.4–3.8	0.3
		3.9–4.3	0.25
		4.4–4.8	0.2
		4.9–5.3	0.15
		5.4–5.6	0.06
		5.7–5.8	0.04
	Distance from rivers and floodplains (m)Subcriteria weights (0.1683)	0–500	0.05
		500–1000	0.10
		1000–1500	0.20
		1500–2000	0.25
		2000–2500	0.30
		>2500	0.35
Infrastructure considerations	Distance to power lines (m)Subcriteria weights (0.4793)	0–1000	0.28	0.153
		1000–5000	0.22
		5000–10,000	0.2
		10,000–20,000	0.14
		20,000–40,000	0.1
		>40,000	0.06
	Distance to power substations (m)Subcriteria weights (0.2940)	0–5000	0.28
		5000–15,000	0.22
		15,000–30,000	0.2
		30,000–50,000	0.15
		50,000–70,000	0.11
		>70,000	0.04
	Distance from airport (m)Subcriteria weights (0.1332)	0–2000	0.02
		2000–10,000	0.16
		10,000–30,000	0.18
		30,000–60,000	0.2
		60,000–100,000	0.21
		>100,000	0.23
	Distance from roads (m)Subcriteria weights (0.0935)	0–2000	0.28
		2000–5000	0.25
		5000–10,000	0.2
		10,000–15,000	0.15
		15,000–20,000	0.1
		>20,000	0.02

4.1.3. Assessing the Influence of Proximity to Power Substations on Wind Farm Suitability

In the strategic siting of wind power plants, the proximity to power substations emerges as a pivotal consideration. These substations serve as vital nodes in the electricity distribution network, facilitating the efficient transmission of power from generation sources to end consumers. The distance from a wind power plant to the nearest substation profoundly influences the overall effectiveness of electricity transmission and distribution systems. To systematically evaluate this criterion, a distance map delineating ranges from power substations was meticulously prepared, spanning from 0 to 90,096 m. This map is segmented into six distinct categories, each assigned a specific weight reflective of its impact on wind power plant siting decisions. Locations closer to substations, categorized as “very close” (0–5000 m), are assigned the highest weight of 0.28, indicating minimal transmission losses and high suitability for wind power plant development. Conversely, areas classified as “very far” (greater than 70,000 m) receive the lowest weight of 0.04, signifying the least favorable conditions due to highest transmission losses. This classification schema ensures that areas closer to power substations are prioritized for wind power plant deployment, optimizing energy transmission efficiency and minimizing costs. By strategically selecting sites in close proximity to substations, wind power projects can enhance their economic viability and contribute meaningfully to a sustainable energy landscape. The distance map depicting the proximity to power substations is presented in Figure 3, and the classification results are summarized in Table 2.

4.1.4. Assessing the Influence of Proximity to Urban Centers on Wind Farm Suitability: A Weighted Distance Analysis

In this study, we utilized the “geometrical interval” method to categorize distances from urban centers into six distinct categories, employing adjusted ranges for ease of understanding. This method ensures equitable representation across categories by consistently increasing each interval by a predetermined ratio. The classification ranges from “very close” to “very far,” with detailed specifications for each category. For instance, “very close” areas, ranging from 0 to 5228 m, are in immediate proximity to urban centers, potentially contributing to significant noise pollution. Conversely, “very far” areas, spanning 43,971–84,613 m, are significantly distant from cities, incurring high transmission costs and presenting logistical challenges for wind farm development. To enhance the analysis, these distance categories were further assessed and ranked based on their ability to balance noise pollution mitigation and transmission cost management. We assigned weights to each category using the AHP, reflecting their suitability for wind farm locations. Moderately close distances, ranging from 7868 to 13,096 m, received the highest weight of 0.30, indicating their optimal balance between transmission costs and noise pollution. Conversely, very close distances, from 0 to 5228 m, were assigned the lowest weight of 0.04, considering their proximity to urban centers and associated challenges. These weighted categories provide valuable insights into selecting appropriate locations for wind farm development, emphasizing the importance of striking a balance between noise pollution and transmission costs (Table 1 and Figure 3).

4.1.5. Assessing the Influence of Proximity to Villages on Wind Farm Suitability: A Weighted Distance Analysis

To determine optimal wind farm locations, it is crucial to strike a balance between mitigating noise pollution near villages and managing transmission costs from distant areas. Thus, the distance from villages is categorized into six segments, ranging from “very close” to “very far,” each carefully considered to achieve this balance effectively. “Very close” areas, within 0–1000 m of villages, pose significant noise pollution risks, while “close” areas (1001–3000 m) still present some noise concerns but to a lesser extent. “Moderately close” regions (3001–5000 m) strike an optimal balance, offering minimal noise disturbance while ensuring accessibility to population centers. Moving further, “moderately far” zones (5001–7000 m) significantly reduce noise pollution while maintaining moderate transmission costs. “Far” areas (7001–8500 m) incur higher transmission expenses but eliminate noise pollution effectively, whereas “very far” regions (8501–9737 m) are rarely viable for wind farms due to logistical challenges and high transmission costs.

This classification system evaluates segments of the study area based on their proximity to villages and suitability for wind farm development. Further precision is added through rankings from “excellent” to “lowest,” considering factors like noise disturbance and transmission expenses. Each category, from “moderately close” to “very close,” is assigned a weight using the AHP, reflecting its significance and potential for wind farm installation. These weights underscore that moderate distances from villages offer the best balance between transmission costs and noise pollution, facilitating informed decisions for wind farm site selection. The mentioned rankings and weights are depicted in Figure 3 and Table 1, respectively.

4.1.6. Assessing the Influence of Road Proximity on Wind Farm Suitability: A Weighted Distance Analysis

The proximity of wind farms to roads is a pivotal factor in their siting, with significant implications for feasibility and operational efficiency. Studies have consistently shown that shorter distances to roads correlate with increased suitability for wind farm development. This phenomenon is primarily attributable to the ease of access to land and the facilitation of infrastructure establishment and grid connectivity. To systematically evaluate this criterion, distances from roads have been categorized into six classes, each assigned a weight reflecting its impact on siting suitability. Closer distances, delineated by categories such as “very close” and “close,” receive higher weights, underscoring their heightened importance in the decision-making process. Conversely, areas classified as “very far” carry lesser weight, indicative of their diminished influence on siting considerations. The classification and weighting system outlined in Table 2 offers a structured framework for assessing road proximity’s impact on wind farm siting. This approach ensures that considerations of accessibility and logistical viability are appropriately integrated into the broader decision-making process. Furthermore, the spatial distribution of distances from roads is visually represented in Figure 4, providing a comprehensive overview of the geographical variation in road proximity across the study area. This visual aid enhances understanding and facilitates informed decision-making regarding wind farm siting strategies.

[figure(s) omitted; refer to PDF]

4.1.7. Mitigating Risks and Assessing Airport Proximity in Wind Farm Siting: A Weighted Distance Analysis

The distance from airports is a crucial criterion in selecting the location for wind power plants, as it can significantly impact operational efficiency and safety. The presence of nearby airports introduces various concerns, including airspace restrictions, safety regulations, and potential interference with radar systems. To thoroughly evaluate this criterion, a classification system based on existing standards and considerations has been devised, categorizing distances into six classes. These classes range from “very close” (0–2000 m) to “very far” (greater than 100,000 m), each assigned a weight reflecting its impact on wind farm siting suitability. For instance, areas classified as “very close” are assigned a weight of 0.02, indicating minimal suitability due to heightened safety and operational concerns, while areas classified as “very far” receive a weight of 0.23, signifying higher suitability due to reduced potential conflicts. The map depicting distances from airports is presented in Figure 3, while Table 2 outlines the classification scheme for distance categories. While the claim regarding the relationship between greater distances from airports and the potential for hosting wind farms requires thorough investigation, the classification system we have implemented provides a robust framework for assessing suitability based on existing standards and considerations. By categorizing distances into six distinct classes and assigning weights accordingly, we aim to provide a comprehensive evaluation of the impact of airport proximity on wind farm siting. However, further research within the context of established standards is essential to validate and refine our classification system, ensuring its reliability in guiding decision-making processes for wind power projects. This approach prioritizes the overall feasibility and sustainability of wind farm developments in selected locations, emphasizing the importance of rigorous scientific inquiry in informing strategic planning and resource allocation.

4.1.8. Slope Classification and Weighting for Optimizing Wind Turbine Placement: A Cost and Efficiency Analysis

To evaluate the potential for wind power plant installation, the slope map of the study area was classified into six categories. The slope classes are as follows: 0%–10%, 10%–20%, 20%–30%, 30%–40%, 40%–50%, and greater than 50%. The classified slope map is shown in Figure 3.

The classification of slope is crucial as lower slopes are more favorable for wind power plant installations. This is primarily due to the reduced cost of installing wind turbines on flatter terrain. In contrast, higher slopes pose significant challenges in terms of construction and maintenance, leading to increased costs and logistical complexities. To quantify the suitability of each slope class, weights were assigned based on their importance using the AHP. The weights were determined considering that lower slopes have a higher potential for wind power plant development due to their lower installation and maintenance costs. The calculated weights for each slope class are presented in Table 1.

The weights were assigned as follows:

0%–10% (excellent): This class received the highest weight, reflecting its superior suitability for wind turbine installation due to minimal installation challenges and costs.

10%–20% (very good): This class also received a high weight, although slightly less than the lowest class, indicating a favorable but slightly more challenging terrain for turbine installation.

20%–30% (good): This class was given a moderate weight, reflecting its intermediate suitability.

30%–40% (moderate): This class received a lower weight, as the increasing slope presents more significant challenges for turbine installation.

40%–50% (low): This class was given an even lower weight due to the steep slope, making turbine installation more complex and costly.

>50% (lowest): This class received the lowest weight, indicating the least suitability for wind power plant installation due to the high costs and difficulties associated with the steep terrain.

The comprehensive analysis and classification of the slope map provide a robust framework for identifying optimal locations for wind power plant installations. The integration of slope data with wind speed potential allows for a more informed and economically viable decision-making process in the selection of wind power plant sites.

4.1.9. Elevation Classification and Weighting: Optimizing Wind Farm Locations Based on Altitude Suitability

In this study, we classified the elevation levels into six categories based on their potential suitability for wind farm installation. The elevation map depicted the distribution of altitude ranges across the study area. Notably, lower elevations were found to have higher potential for wind farm development due to their lower installation and maintenance costs. Conversely, higher elevations were associated with lower potential, as accessibility and operational expenses increase with altitude. The elevation classification, illustrated in Figure 3, provided a comprehensive understanding of the terrain characteristics within the study area. The categories ranged from “lowest” to “highest,” each representing specific altitude intervals. The classification aimed to assist decision-makers in identifying optimal locations for wind energy projects based on terrain suitability.

Furthermore, we assigned weights to each elevation category to quantify their significance in the site selection process. The weights, as presented in Table 1, reflected the relative importance of each elevation range. These weightings were crucial in the decision-making process, as they guided the prioritization of potential sites based on their elevation characteristics.

Overall, the elevation classification and weighting process yielded valuable insights into the suitability of different terrain types for wind farm development. These results serve as a foundational basis for further analysis and discussion in determining the most favorable locations for wind energy projects.

4.1.10. Earthquake Magnitude Classification and Weighting for Optimal Wind Farm Siting: A Seismic Risk Mitigation Approach

The seismic hazard mapping plays a pivotal role in determining suitable locations for wind energy power plants, as outlined in this research study. Utilizing earthquake data spanning from 1907 to 2024 obtained from the https://earthquake.usgs.gov/earthquak… website, a comprehensive map depicting the distribution of earthquake magnitudes across the study area was generated using geostatistical techniques. This map was then categorized into six distinct classes, each assigned a corresponding weight. The significance of earthquake magnitude zoning maps in wind power plant siting cannot be overstated, as they aid in identifying regions susceptible to major seismic events, thereby averting construction activities in high-risk zones. Standards such as the American Society of Civil Engineers (ASCE 7) and Eurocode 8 offer valuable guidelines for classifying earthquake magnitudes. Based on these standards and existing research, earthquake magnitudes were classified into six classes, with higher magnitudes indicating lower suitability for wind power plant deployment. Each class was assigned a weight using the AHP, with lower earthquake risk areas receiving higher weights due to their reduced risk profile for wind power plant installation. This proposed classification and weighting scheme facilitate the identification of optimal sites for wind power plants, mitigating earthquake-related hazards and optimizing siting decisions. The earthquake magnitude classification map and associated weightings are detailed in Figure 5 and Table 2, respectively.

[figure(s) omitted; refer to PDF]

4.1.11. Assessing Fault Line Proximity for Wind Power Plant Siting: A Classification and Weighting Approach Based on Standards

In the siting of wind power plants, the distance from fault lines is considered a critical criterion. Existing standards such as ASCE 7, Eurocode 8, and the Iranian National Building Code (Standard 2800) recommend that essential and vital structures be situated at a specific distance from active fault lines to mitigate earthquake risks. These standards and related seismic hazard assessment studies assist in determining safe distances for construction. In this study, based on the existing standards, the distance from fault lines has been categorized into six classes: very close (0–2 km), close (2–5 km), moderately close (5–10 km), moderately far (10–20 km), far (20–30 km), and very far (over 30 km). The classification map of the distances from fault lines is presented in Figure 5. For each of these classes, appropriate weights have been calculated using the AHP, with the results shown in Table 2. This classification and weighting facilitate the evaluation of different areas based on their distance from fault lines and aid in selecting the most suitable locations for wind power plant construction. Consequently, this approach helps in reducing earthquake risks and optimizing the siting of wind power plants.

4.1.12. Flood Risk Assessment and Weighting for Wind Farm Siting: A Proximity-Based Classification Approach

In the context of selecting optimal sites for wind power plants, the distance from rivers and floodplains is a critical criterion. Flooding poses significant risks to wind power plants and their turbines, making it essential to consider this factor in site selection. To address this, a map of the river network was extracted from a DEM with a 28-m resolution. Subsequently, a distance map was created, indicating the proximity to rivers and potential floodplains. This distance ranges from a minimum of 0 m to a maximum of 11,319 m (Figure 5).

Based on previous studies and established standards, the distance map was classified into six categories, each assigned a specific weight to reflect the varying levels of flood risk. Areas very close to rivers (0–500 m) were assigned a weight of 0.05, indicating a high risk of flooding and the least suitability for wind power plant siting. Locations close to rivers (500–1000 m) received a weight of 0.10, representing a moderate to high flood risk, making them less suitable for wind power plants. Moderately close areas (1000–1500 m) were given a weight of 0.20, indicating moderate flood risk and requiring careful consideration before siting wind power plants. Moderately far areas (1500–2000 m) with a weight of 0.25 signify low to moderate flood risk and are generally suitable for wind power plants. Far areas (2000–2500 m) were assigned a weight of 0.30, representing low flood risk and making them suitable for wind power plants. Very far areas (greater than 2500 m) received the highest weight of 0.35, indicating very low flood risk and high suitability for wind power plant siting (Table 2 and Figure 6).

[figure(s) omitted; refer to PDF]

This classification ensures that areas with a lower risk of flooding are prioritized for the installation of wind power plants, thereby enhancing the safety and operational stability of the turbines and associated infrastructure. The detailed classification and weighting facilitate a strategic approach to minimizing flood-related hazards in wind power plant site selection.

4.1.13. Land Use Analysis for Wind Farm Siting: Classification and Weighting for Optimal Site Selection

For the purpose of wind farm site selection, the land use map needs to be classified into six classes from “excellent” to “lowest.” This classification should balance optimal land use and minimize negative impacts on the environment and society. The land use map of the study area, created using Landsat 9 data for the year 2023, includes eight land use classes: bare ground, rangeland, crops, flooded vegetation, trees, snow, water, and built area. The classification and weighting of each class are as follows (Figure 5): Bare ground areas are considered the best option for wind farm development due to the absence of natural obstacles and lower installation costs, thus receiving the highest weight (0.30). Rangelands, with minimal vegetation cover and suitability for turbine installation, are classified as “very good” with a weight of 0.25. Agricultural areas, due to potential impacts on crops and the need for coordination with farmers, are classified as “good” with a weight of 0.20 (Table 1). Flooded vegetation areas, due to moisture and installation challenges, are placed in the “moderate” category with a weight of 0.10. Tree-covered areas, because of the presence of vegetation and the need for management, are classified as “low” with a weight of 0.08. Snow-covered areas, given access difficulties and challenges posed by winter conditions, fall into the “lowest” category with a weight of 0.07. Finally, water bodies and built areas are deemed the least suitable for turbine installation due to physical and legal obstacles, receiving a weight of zero (Table 1).

This classification and weighting effectively highlight the significance of each land use type for wind farm development and can be utilized in further analyses for optimal site selection. This approach reflects a balanced strategy between land utilization and mitigating environmental and social impacts, which is crucial for the success of wind farm projects.

4.2. Determining and Validating Criteria Weights Using AHP for Wind Power Plant Siting

In this study, the AHP was used to determine the relative weights of five key criteria for the siting of a wind power plant. The criteria considered were wind resource assessment, site characteristics, environmental and social factors, geotechnical considerations, and infrastructure considerations. Given the critical importance of wind availability to the functioning of a wind power plant, we ensured that the weight assigned to the wind resource assessment was significantly higher than the other criteria while also ensuring that the weights of all criteria were meaningful and logical. We constructed a pairwise comparison matrix based on expert input and our understanding of the relative importance of each criterion.

Using the AHP methodology, we calculated the normalized weights and verified the consistency of our pairwise comparisons. The final weights were wind resource assessment (0.46), site characteristics (0.153), environmental and social factors (0.102), geotechnical considerations (0.153), and infrastructure considerations (0.153). To ensure the reliability of our comparisons, we calculated the CR. The Ci was computed as follows:$$\begin{array}{}\text{(20)}& Ci=\frac{{\lambda }_{\max }-n}{n-1}=\frac{4.9982-5}{5-1}=-0.00045,\end{array}$$$$\begin{array}{}\text{(21)}& \text{CR}=\frac{Ci}{\text{RI}}=\frac{-0.00045}{1.12}=-0.0004\end{array}$$

Since the CR is less than 0.1, the pairwise comparison matrix is considered consistent, indicating a high level of consistency in our judgments. These results confirm that wind resource assessment is the most critical factor in the siting of a wind power plant, followed by geotechnical considerations, infrastructure considerations, and site characteristics, with environmental and social factors also playing a significant but relatively lesser role. This consistent and logical distribution of weights supports informed decision-making in the site selection process for wind power plants.

4.3. Evaluation of Wind Power Potential Using MCDM Models

This section evaluates the potential of the study area for wind power plant development using MCDM models, particularly the TOPSIS and VIKOR methods. These models effectively address complex decision-making problems involving multiple conflicting criteria. The analysis integrates spatial data and weighted criteria across five main dimensions: wind resource assessment, site characteristics, environmental and social factors, geotechnical considerations, and infrastructure considerations. By applying these models, optimal locations for wind power plants are identified, balancing technical, environmental, and economic factors. Details of the spatial modeling process and result validation are provided in Sections 4.3.1 and 4.3.2.

4.3.1. Spatial Analysis of Wind Power Potential Using TOPSIS and VIKOR Methods

After creating the spatial distribution map of the subindices (13 subindices depicted in Figures 3–8), MCDM methods (TOPSIS and VIKOR) were employed for the spatial modeling of the potential of the studied area for a wind power plant. In the modeling process, to assess the area’s potential, the subindices within each of the five main dimensions (wind resource assessment, site characteristics, environmental and social factors, geotechnical considerations, and infrastructure considerations) were evaluated separately. This was achieved using MCDM methods and considering the relative importance (weight) of each subcriterion. These subindices were then combined to determine the potential of the area for the wind power plant in each pixel across the five dimensions individually.

[figure(s) omitted; refer to PDF]

After separately evaluating the area’s potential in these five dimensions, a holistic assessment combining measurements and one-dimensional judgments was needed to evaluate the potential in terms of all dimensions together. Consequently, for continued spatial modeling, spatial distribution maps of the potential for each dimension (considering the relative weight of each main criterion) were integrated using the TOPSIS and VIKOR models. This integration aimed to determine the potential of each pixel in the study area for the wind power plant across all five dimensions. The outcomes of these two multicriteria decision models are presented in Figure 3.

4.3.2. Integration and Validation of MCDM Results for Wind Power Site Selection

In this study, two different MCDM models were used to evaluate the potential of the studied area for a wind power plant. Given the unique characteristics, limitations, and assumptions of each MCDM method, their results vary. As shown in Figure 7, the spatial distribution of the wind power plant potential differs between the two methods. To assess the reliability of the results from the TOPSIS and VIKOR models, Pearson’s correlation test was employed, and the results are presented in Table 3. The correlation test indicates a significant correlation (97%) between the two models’ results, demonstrating their reliability.

Table 3

Pearson correlation between the wind power plant site selection results using TOPSIS and VIKOR models.

Correlation
Decision-making models			TOPSIS	VIKOR
Pearson correlation coefficient	TOPSIS	Correlation coefficient	1	${0.97179}^{\ast \ast }$
		Sig. (two-tailed)	0	0.000
		N	1,952,148	1,952,148
	VIKOR	Correlation coefficient	${0.97179}^{\ast \ast }$	1
		Sig. (two-tailed)	0.000	0
		N	1,952,148	1,952,148

${\hspace{0.17em}}^{\ast \ast }$Correlation is significant at the 0.01 level (two-tailed).

The integration of the results from both decision-making methods enhances the validity of the potential assessment of the studied area for the wind power plant. Thus, after confirming the reliability of the different methods’ results, the potential spatial distribution maps were combined using the averaging method, with the outcome shown in Figure 8. Subsequently, the Robert Prescott-Allen model was used to reclassify the potential spatial map. This model standardizes values between 0 and 1, representing different potential levels for the wind power plant. In this study, values were classified between 0 and 1, with intervals as described in Table 4.

Table 4

Wind power potential in the region based on land suitability classification.

Level of land suitability	Wind farm land suitability classification	Percentage of area per class
0–0.2	Lowest	2.84
0.21–0.4	Low	34.63
0.41–0.60	Moderate	42.45
0.61–0.80	Good	14.82
0.81–1	Excellent	5.26

The classified potential map (Figure 8) shows the minimum suitability level as 0.0553 and the maximum as 0.959. Analysis of the potential classification map reveals that 2.84% of the studied area is very unsuitable, 34.63% has low potential, 42.45% is in the middle class, 14.82% has good potential, and 5.26% has excellent suitability (Table 4).

The results indicate that the fourth and fifth classes are the most suitable for establishing wind power plants. The average wind speed, the primary factor for wind power plants, is favorable in these two classes, with wind speeds ranging from 5 to 14 m/s. Other criteria in these classes also show good potential. These two classes cover ~20% of the total study area. As depicted in Figure 6, areas in the good class are shown in yellow, while those with very good potential are in brown.

Table 4 shows that the third class, or middle class, covers about 42.45% of the total study area. Further investigation reveals that only 8.5% of this class has wind speeds exceeding 5 m/s, making them more suitable for wind power plants, while the remaining area is less suitable due to lower wind speeds. Consequently, only 8.5% of the middle class has moderate suitability based on wind speed. Overall, it was found that 71.3% of the study area is unsuitable for wind power plants.

The study also involved evaluating the modeling results from the perspectives of researchers and electrical engineering experts. A specialized meeting was held with 15 electrical engineering experts. Prior to the meeting, a questionnaire covering various stages of the research was distributed among the participants. During the meeting, the research stages and results were presented and discussed. Expert opinions were collected both orally and via the questionnaire. The summarized expert opinions revealed that the evaluation results of the studied area’s potential for wind power plants were consistent with expert views across all five dimensions. Experts’ responses indicated that the research results were ~90% consistent with ground reality.

4.4. Quantifying Wind Energy Potential: Modeling Results and Electricity Generation Estimates

Finally, the last stage encompasses analyzing the results of the modeling and calculating the potential electricity generation from the wind power plant in the examined area. In the first step, the spatial potential map of the wind power plant (Figure 9), derived from combining the MCDA models (TOPSIS and VIKOR), was used to isolate the areas classified as good and excellent (classes 4 and 5). These two classes represent the most suitable locations for installing wind turbines and developing wind power plants. The remaining classes were excluded from further analysis. Then, the power density map was generated using Equation (2), which is provided in Section 3.3.7.

[figure(s) omitted; refer to PDF]

Subsequently, this power density map, the same geographic areas that were classified as good and excellent, was extracted for further analysis. These areas, identified from the spatial potential map, were cross-referenced with the power density map to calculate the potential electricity generation for each pixel. The potential electricity generation for each pixel was computed (Equation (3)), which is provided in Section 3.3.7.

The results, presented in Figure 9, reveal that the minimum power generation potential in the selected pixels is 0.23 MW, while the maximum reaches 27.5 MW. This indicates that in certain pixels, a wind turbine with a 100-m height and a 120-m rotor diameter (as assumed in this study) can generate between 0.23 and 27.5 MW of electricity. The total area deemed suitable for wind turbine installation and wind power plant development within the study region is ~8277 km².

Previous studies have shown that the distance between wind turbines should be five times the rotor diameter [50]. Based on this guideline, the study area can accommodate the installation of 23,000 turbines. According to the calculations made in this research, the region has the capacity to generate 48,522 MW of electricity. It is worth noting that among these 23,000 locations, 3332 sites have the potential to generate 3 MW or more, while the remaining locations have lower generation potential. The combined potential of these 3332 high-capacity sites is ~13,993 MW.

The results of this study highlight the significant potential for generating renewable energy through wind power in the study area. With a total installed capacity of 48,522 MW, this region can contribute substantially to the development of sustainable energy infrastructure. Specifically, the identification of 3332 sites capable of producing 3 MW or more underscores the opportunity for large-scale wind power plants, which could play a crucial role in mitigating carbon emissions and reducing the reliance on fossil fuels.

Emerging the desired area for wind energy reduces greenhouse gas emissions to a large extent and the consumption of fossil fuels. For example, 42.9 TWh of electricity may be produced yearly from 13,993 MW of wind power generated at a typical capacity factor of 35%. This yields savings of 5.8 million tons of natural gas yearly when the conversion energy system works with an average efficiency of 50%, and the electricity is generated from natural gas. Furthermore, this causes the elimination of the release of roughly 16 million tons of CO₂ annually. Wind energy is shown to be an environmentally friendly alternative to fossil fuels [61, 62].

Moreover, environmental factors such as fault lines, flood zones, and proximity to residential areas were evaluated using spatial analysis techniques. These factors were integrated into the holistic assessment framework to minimize environmental risks and ensure sustainable site selection for wind power plants. The spatial analysis results revealed that areas with higher wind potential were often located at a safe distance from environmental hazards, reinforcing the robustness of the assessment criteria.

Given that wind power is a clean and RES, the establishment of wind power plants in the identified suitable areas would not only help in meeting energy demands but also contribute to the global efforts to combat climate change by reducing greenhouse gas emissions. The results of this study are fundamental and highly applicable, providing a valuable framework for decision-makers and policymakers involved in energy planning. Furthermore, the study demonstrates the importance of integrating MCDM methods such as TOPSIS and VIKOR with spatial analysis techniques to optimize site selection for wind power plants, ensuring that both environmental and technical considerations are addressed comprehensively. In summary, this research offers a solid foundation for future wind power projects and highlights the potential of the study area as a key location for renewable energy development, contributing to both local and global energy sustainability goals. For a comprehensive overview of the decision-making process, site selection criteria, and technical calculations, see Appendices A, B, and C.

4.5. Sensitivity Analysis of Wind Farm Potential and Electricity Generation Outcomes

The weight of criteria and their values are crucial elements considered in sensitivity analysis. In this study, objective and measurable methods were employed to produce the indicators, thereby placing greater sensitivity on the weight of the indicators. The weight of criteria serves as a foundational basis for judgment in decision-making processes. Hierarchical analysis was utilized to determine the relative weight of the criteria, and the CR was used to evaluate the precision and accuracy of the weighting. The CR in the hierarchical analysis was calculated to be less than 0.1, indicating a high degree of convergence in the responses provided by individuals involved in the process [56, 63]. In addition to the weight of the criteria, various solutions and methods were employed to enhance the validity of MCDM results [63–65]. These include the following:

1. Utilization of diverse indicators: To ensure comprehensive coverage of all dimensions and components relevant to the evaluation of the studied area’s potential for a wind power plant, 13 indicators were selected. These indicators were chosen based on previous studies, international standards, and expert opinions. The use of diverse indicators, even if they are not perfectly aligned, enhances the validity of the evaluation results [63–65].

2. Application of multiple decision-making models: Two MCDM models, TOPSIS and VIKOR, were employed to mitigate the limitations inherent in each model. Each method has unique characteristics, limitations, and assumptions; hence, the use of multiple methods increases the validity of the research outcomes [56, 63].

3. Reliability check using Pearson correlation: The similarity of results obtained from different decision-making models was assessed using the Pearson correlation model. The spatial distribution of the potential for wind power plants in the studied area, as determined by the TOPSIS and VIKOR models, showed relative differences. The Pearson correlation test demonstrated a significant correlation of 97% between the results of the two models, indicating high reliability. Consequently, integrating the results of the MCDM methods through averaging enhanced the validity of the potential map for the studied area, yielding a comprehensive and conclusive outcome [63–65].

Thus, in the holistic assessment, each of the 13 indicators was integrated using a weighted multicriteria analysis approach. This method ensured that the environmental, technical, economic, and social dimensions were adequately balanced. For instance, indicators like wind speed and proximity to power grids received higher weights due to their critical role in energy production, while environmental and social criteria were prioritized to align with sustainability goals. By combining these factors, the assessment provided a comprehensive and balanced evaluation of the study area.

4.6. Comparison With Similar Studies: Methodological Advancements and Superior Results

This study employs GIS and MCDM methods (AHP, VICOR, and TOPSIS) to conduct a comprehensive and accurate assessment of wind farm potential. By integrating the results of these models, the accuracy and validity of the modeling process have been significantly enhanced. This approach is innovative compared to similar studies that often rely on a single decision-making model. Additionally, utilizing Landsat 9 satellite data and geostatistical methods, a more comprehensive approach has been adopted to evaluate environmental and geological factors. While previous studies have primarily focused on general factors such as wind speed, distance from transmission lines, and population centers, this study considers factors like distance from faults, flood zones, and airports to provide a more accurate assessment of associated risks. Furthermore, sensitivity analysis was conducted on the results to evaluate the robustness and validity of the models. In terms of quantitative results, this research indicates that the percentage of suitable areas for wind farm development in the study region is significantly higher than in similar studies of neighboring regions. This highlights the region’s high potential for wind energy generation. Moreover, while previous studies often limited their scope to assessing the overall potential of the region, this research provides a more comprehensive picture of wind energy development potential by considering economic and environmental factors.

Moreover, the use of both TOPSIS and VIKOR models provided complementary perspectives on wind energy potential. While the TOPSIS model emphasizes closeness to the ideal solution by minimizing the distance from positive criteria, the VIKOR model balances the best and worst alternatives by introducing a compromise ranking. The integration of these models enhanced the robustness of the results by reducing potential biases associated with using a single method. This dual-model approach aligns with similar studies in renewable energy planning, where consistency across decision-making approaches is critical for reliable outcomes.

4.7. Comprehensive Holistic Assessment of Wind Energy Potential

A comprehensive and holistic assessment of wind energy potential in the KRG was conducted by integrating spatial, environmental, social, and infrastructure-related criteria. The study employed advanced MCDM models to assess the suitability of potential wind farm locations. Thirteen subcriteria were grouped into five key dimensions: wind resource assessment, site characteristics, environmental and social factors, geotechnical considerations, and infrastructure considerations.

Each of these dimensions was thoroughly analyzed using GISs, ensuring that spatial and nonspatial data were incorporated into the evaluation. The weighting of each criterion was performed using the AHP, which allowed for a transparent and systematic consideration of the relative importance of each factor.

To ensure robustness, two widely used MCDM models, TOPSIS and VIKOR, were applied. The TOPSIS model helped identify locations closest to the ideal solution, while the VIKOR model provided a compromise solution, balancing the conflicting criteria. Results from both models were integrated using an averaging method to generate a final suitability map. Sensitivity analysis was conducted to verify that the weighting of criteria and model integration did not introduce significant biases, confirming the robustness and reliability of the results.

The final suitability map, classified using the Robert Prescott-Allen model, categorized the study area into five suitability levels, ranging from very unsuitable to excellent. About 21% of the study area (8277 km²) was identified as having good to excellent potential for wind energy development. These findings suggest that the KRG holds significant promise for becoming a regional hub for wind energy production, potentially reducing reliance on fossil fuels and contributing to climate change mitigation.

5. Study Limitations

Despite providing a comprehensive framework for assessing wind energy potential in the KRG, the present study is not without limitations. The most significant constraints include uncertainties in input data, particularly wind speed data, and a lack of consideration for the full range of interactions between various factors. The study also faces temporal and spatial data limitations. Additionally, the assessment does not fully address the economic feasibility of projects nor does it consider long-term social and environmental impacts. Although these limitations exist, the findings serve as a valuable starting point for future research and decision-making related to renewable energy development in the region.

6. Conclusions

This research has focused on identifying and accurately assessing the vast potential of wind energy in the KRG of Iraq, representing a significant step toward sustainable energy development in the area. By integrating modern spatial analysis and MCDM methods, including remote sensing data, GIS, and the TOPSIS and VIKOR decision-making models, a comprehensive model for evaluating wind energy production potential has been developed.

The results indicate that ~21% of the study area, covering around 8277 km², has very good to good potential for wind energy production. These areas possess the capacity to generate over 48,000 MW of electricity, with the identification of 3332 sites capable of producing more than 3 MW, underscoring the significant potential of this region for developing large-scale wind power plants. Environmental factors such as land topography, distance from fault lines, and proximity to residential areas significantly impact wind energy potential, highlighting the importance of considering these factors in decision-making related to wind power plant construction.

The innovations of this research include the simultaneous application of multiple analytical methods and the consideration of environmental and technical factors, which enhance the accuracy and reliability of the results. Additionally, the high-precision potential maps generated provide a powerful tool for planners and investors. Given the importance of developing clean energy and reducing greenhouse gas emissions, the findings of this research can serve as a roadmap for achieving sustainable development goals in the region.

It is recommended that future studies focus on conducting detailed economic assessments of wind energy projects, examining the environmental and social impacts of these projects, and developing more precise predictive models for wind energy production. Furthermore, conducting economic studies to evaluate the feasibility of wind energy projects and investigating the environmental impacts of wind power plant construction on the local environment are suggested.

Overall, this research demonstrates the high potential of the KRG to become one of the key centers for wind energy production in the region and can serve as a scientific and practical foundation for future studies in this field. Furthermore, the holistic assessment approach adopted in this study demonstrates its ability to address all critical dimensions—environmental, technical, economic, and social—in a comprehensive manner. By integrating spatial analysis with MCDM models such as TOPSIS and VIKOR, the study offers a robust framework for evaluating wind energy potential. This methodology not only identifies optimal locations for wind power plants but also provides decision-makers with a practical tool for sustainable energy planning. The framework can serve as a replicable model for similar assessments in other regions, contributing to global efforts in renewable energy development and environmental conservation.

Ethics Statement

This research does not include human studies.

Author Contributions

Loghman Khodakarami: writing–original draft, methodology, results and discussion, map preparation, modeling. Khidhir Dara Khalid: wind energy analysis, writing–review and editing. Ali Jafar Abdullah: writing–review and editing, wind energy analysis. Rustum Jehan Mahmmod: introduction, methodology, criteria mapping, writing–review and editing. Asaad Frya Rebwar: introduction, methodology, criteria mapping, writing–review and editing. Shawkat Aya Bakhtyar: introduction, methodology, criteria mapping, writing–review and editing. Khudadad Zulfa Jalil: introduction, methodology, criteria mapping, writing–review and editing.

Acknowledgments

The authors would like to express their gratitude for the opportunity to conduct this research. While no direct assistance was provided during the study, the authors appreciate the valuable resources and literature that informed their work. This research contributes to the understanding of wind energy potential in the Kurdistan Region of Iraq and aims to support future developments in sustainable energy.

Appendix

Appendix A: Nomenclature—Decision-Making Methods and Parameters

Symbol	Parameter	Description
AHP	Analytic hierarchy process	A multicriteria decision-making method used to assess criteria weights
VIKOR	VIKOR	A decision-making model used to rank and select from alternatives
TOPSIS	TOPSIS	A method used for ranking and selecting from a set of alternatives
S+	Positive ideal solution distance	Euclidean distance to the positive ideal solution in TOPSIS
S−	Negative ideal solution distance	Euclidean distance to the negative ideal solution in TOPSIS
Ci	Closeness coefficient	Relative closeness to the ideal solution in TOPSIS
fj∗	Ideal value for criterion jj	Best value for criterion j in VIKOR
fj−	Anti-ideal value for criterion jj	Worst value for criterion j in VIKOR
Sj	Weighted sum of distances in VIKOR	Aggregated measure of distance to the ideal solution in VIKOR
Rj	Maximum regret in VIKOR	Maximum deviation from the ideal solution in VIKOR
Qj	Compromise measure in VIKOR	Balanced measure of Sj and Rj in VIKOR
ν	Weighting factor in VIKOR	Factor balancing group utility and individual regret in VIKOR
λmax	Maximum eigenvalue	Largest eigenvalue in AHP pairwise comparison matrix
Ci	Consistency index	Measure of consistency in AHP pairwise comparisons
CR	Consistency ratio	Ratio of CI to random index in AHP
RI	Random index	Index used to calculate CR in AHP
wj	Weight of criterion jj	Importance weight assigned to criterion j in MCDM
dij	Decision matrix element	Value of alternative i for criterion j
rij	Normalized decision matrix element	Normalized value of dij
vij	Weighted normalized value	Weighted value of rij

Appendix B: Nomenclature—Criteria for Wind Farm Site Selection, Spatial and Environmental Factors

Symbol	Parameter	Unit	Description
V	Wind speed	m/s	Wind speed at a specific height
S	Slope	%	Slope of the terrain, affecting turbine installation and maintenance costs
H	Altitude	m	Elevation above sea level, influencing construction and operational efficiency
Dv	Distance from villages	m	Minimum distance from residential areas to reduce noise and social impacts
Dt	Distance from towns	m	Distance from urban centers to balance energy distribution and urban expansion
Lu	Land use	—	Type of land use (e.g., bare ground, rangeland, and forests) affecting suitability
Df	Distance from fault lines	m	Safe distance from geological faults to ensure structural safety
Me	Earthquake magnitude	mag	Magnitude of earthquakes, indicating seismic risk for the site
Dr	Distance from rivers and floodplains	m	Safe distance from water bodies to avoid flooding risks
Dpl	Distance to power lines	m	Proximity to existing power lines to reduce connection costs
Dps	Distance to power substations	m	Distance to power substations for efficient energy transmission
Drd	Distance from roads	m	Proximity to roads for ease of transportation and maintenance

Appendix C: Nomenclature—Wind Energy Technical Parameters

Symbol	Parameter	Unit	Description
V	Wind speed	m/s	Wind speed at a specific height
V1	Wind speed at height Z1	m/s	Wind speed measured at reference height Z1
V2	Wind speed at height Z2	m/s	Wind speed at desired height Z2
Z1	Reference height	m	Height at which wind speed V1 is measured
Z2	Desired height	m	Height at which wind speed V2 is calculated
n	Wind shear coefficient	—	Exponent representing the effect of surface roughness on wind speed
Pd	Power density	W/m2	Power per unit area generated by wind
ρ	Air density	kg/m3	Density of air at a given altitude and temperature
A	Swept area of wind turbine blades	m2	Area covered by the rotating blades of a wind turbine
CP	Power coefficient	—	Efficiency factor of a wind turbine (typically 0.40)
P	Power generated by a turbine	W	Total power output of a wind turbine
Asuitable	Suitable area for wind farm	km2	Area identified as suitable for wind energy development
Pwind	Wind power potential	MW	The potential for wind power generation in the identified areas
Rotor diameter	Rotor diameter	m	The diameter of the rotor of the wind turbine
Hub height	Hub height	m	The height of the hub of the wind turbine from the ground
Turbine power output	Turbine power output	MW	The amount of electricity produced by the wind turbine
Efficiency of conversion	Efficiency of conversion	%	The efficiency with which wind energy is converted into electrical energy by the wind turbine
Area of potential wind farm sites	Area of potential wind farm sites	km2	Area identified as suitable for wind farm development based on the criteria selection

References

[1] M. I. D. Zulkifly, M. S. M. Said, "Determining Optimal Solar Power Plant Location in Melaka, Malaysia: A GIS-Based Solutions," IOP Conference Series: Earth and Environmental Science, vol. 1051, 2022.

[2] S. Rekik, S. El Alimi, "Optimal Wind-Solar Site Selection Using a GIS-AHP Based Approach: A Case of Tunisia," Energy Conversion and Management X, vol. 18, 2023.

[3] C. Kocabaldir, M. A. Yücel, "GIS-Based Multi-Criteria Decision Analysis of Site Selection for Photovoltaic Power Plants in Çanakkale Province," International Journal of Environment and Geoinformatics, vol. 7 no. 3, pp. 347-355, DOI: 10.30897/ijegeo.689570, 2020.

[4] W. Xiaowu, H. Ben, "Exergy Analysis of Domestic-Scale Solar Water Heaters," Renewable and Sustainable Energy Reviews, vol. 9 no. 6, pp. 638-645, DOI: 10.1016/j.rser.2004.04.007, 2005.

[5] A. Kumar, T. C. Kandpal, "Solar Drying and CO 2 Emissions Mitigation: Potential for Selected Cash Crops in India," Solar Energy, vol. 78 no. 2, pp. 321-329, DOI: 10.1016/j.solener.2004.10.001, 2005.

[6] M. Kabak, G. Taşkinöz, "Determination of the Installation Sites of Wind Power Plants With Spatial Analysis: A Model Proposal," Sigma Journal of Engineering and Natural Sciences, vol. 38 no. 1, pp. 441-457, 2020.

[7] A. Tunc, G. Tuncay, Z. Alacakanat, F. Sevimli, "GIS Based Solar Power Plants Site Selection Using Analytic Hierarchy Process (AHP) In Istanbul, Turkey," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, pp. 1353-1360, 2019.

[8] I. B. Fridleifsson, "Geothermal Energy for the Benefit of the People," Renewable and Sustainable Energy Reviews, vol. 5 no. 3, pp. 299-312, DOI: 10.1016/S1364-0321(01)00002-8, 2001.

[9] E. Chamanehpour, "Site Selection of Wind Power Plant Using Multi-Criteria Decision-Making Methods in GIS: A Case Study," Computational Ecology and Software, vol. 7 no. 2, 2017.

[10] F. M. I. Flora, N. Donatien, R. Tchinda, O. Hamandjoda, "Selection Wind Farm Sites Based on GIS Using a Boolean Method: Evaluation of the Case of Cameroon," Journal of Power and Energy Engineering, vol. 9 no. 1,DOI: 10.4236/jpee.2021.91001, 2021.

[11] W. Tong, Wind Power Generation and Wind Turbine Design, 2010.

[12] E. Feloni, E. Karandinaki, "GIS-Based MCDM Approach for Wind Farm Site Selection—A Case Study," Journal of Energy and Power Technology, vol. 3 no. 3,DOI: 10.21926/jept.2103039, 2021.

[13] S. H. Taha, S. B. Abdalla, "Utilizing GIS and RS to Determine Suitable Areas for Installing Wind Power Stations: Case Study, Erbil Governorate, Kurdistan Region, Iraq," Journal of Kurdistani for Strategic Studies, vol. no. 8, 2024.

[14] S. Opricovic, G.-H. Tzeng, "Compromise Solution by MCDM Methods: A Comparative Analysis of VIKOR and TOPSIS," European Journal of Operational Research, vol. 156 no. 2, pp. 445-455, DOI: 10.1016/S0377-2217(03)00020-1, 2004.

[15] G. M. Joselin Herbert, S. Iniyan, E. Sreevalsan, S. Rajapandian, "A Review of Wind Energy Technologies," Renewable and Sustainable Energy Reviews, vol. 11 no. 6, pp. 1117-1145, DOI: 10.1016/j.rser.2005.08.004, 2007.

[16] K. Koçak, "A Method for Determination of Wind Speed Persistence and Its Application," Energy, vol. 27 no. 10, pp. 967-973, DOI: 10.1016/S0360-5442(02)00033-6, 2002.

[17] D. Weisser, "A Wind Energy Analysis of Grenada: An Estimation Using the ‘Weibull’ Density Function," Renewable Energy, vol. 28 no. 11, pp. 1803-1812, DOI: 10.1016/S0960-1481(03)00016-8, 2003.

[18] G. Li, "Feasibility of Large Scale Offshore Wind Power for Hong Kong—a Preliminary Study," Renewable Energy, vol. 21 no. 3-4, pp. 387-402, DOI: 10.1016/S0960-1481(00)00038-0, 2000.

[19] R. M. R. Kainkwa, "Wind Speed Pattern and the Available Wind Power at Basotu, Tanzania," Renewable Energy, vol. 21 no. 2, pp. 289-295, DOI: 10.1016/S0960-1481(00)00076-8, 2000.

[20] S. Vartan, "What Is Wind Energy? Definition and How It Works," 2021. https://www.treehugger.com/what-is-wind-energy-5097217

[21] J. J. W. Watson, M. D. Hudson, "Regional Scale Wind Farm and Solar Farm Suitability Assessment Using GIS-Assisted Multi-Criteria Evaluation," Landscape and Urban Planning, vol. 138, pp. 20-31, DOI: 10.1016/j.landurbplan.2015.02.001, 2015.

[22] S. H. Siyal, U. Mörtberg, D. Mentis, M. Welsch, I. Babelon, M. Howells, "Wind Energy Assessment Considering Geographic and Environmental Restrictions in Sweden: A GIS-Based Approach," Energy, vol. 83, pp. 447-461, DOI: 10.1016/j.energy.2015.02.044, 2015.

[23] A. Bennui, P. Rattanamanee, U. Puetpaiboon, P. Phukpattaranont, K. Chetpattananondh, "Site Selection for Large Wind Turbine Using GIS," pp. 561-566, .

[24] A. Gerbo, K. V. Suryabhagavan, T. Kumar Raghuvanshi, "GIS-Based Approach for Modeling Grid-Connected Solar Power Potential Sites: A Case Study of East Shewa Zone, Ethiopia," Geology, Ecology, and Landscapes, vol. 6 no. 3, pp. 159-173, DOI: 10.1080/24749508.2020.1809059, 2022.

[25] M. A. Baseer, S. Rehman, J. P. Meyer, M. M. Alam, "GIS-Based Site Suitability Analysis for Wind Farm Development in Saudi Arabia," Energy, vol. 141, pp. 1166-1176, DOI: 10.1016/j.energy.2017.10.016, 2017.

[26] A. Çalık, "A Hybrid Approach for Selecting Sustainable Suppliers and Determining Order Allocation Based on Interval Type-2 Fuzzy Sets," Journal of Enterprise Information Management, vol. 33 no. 5, pp. 923-945, DOI: 10.1108/JEIM-09-2019-0302, 2020.

[27] T. R. Ayodele, A. S. O. Ogunjuyigbe, O. Odigie, A. A. Jimoh, "On the Most Suitable Sites for Wind Farm Development in Nigeria," Data in Brief, vol. 19, pp. 29-41, DOI: 10.1016/j.dib.2018.04.144, 2018.

[28] L.-I. Tegou, H. Polatidis, D. A. Haralambopoulos, "Environmental Management Framework for Wind Farm Siting: Methodology and Case Study," Journal of Environmental Management, vol. 91 no. 11, pp. 2134-2147, DOI: 10.1016/j.jenvman.2010.05.010, 2010.

[29] M. A. Nazari, M. E. H. Assad, S. Haghighat, A. Maleki, "Applying TOPSIS Method for Wind Farm Site Selection in Iran," .

[30] E. Ilbahar, S. Cebi, C. Kahraman, "A State-of-the-Art Review on Multi-Attribute Renewable Energy Decision Making," Energy Strategy Reviews, vol. 25, pp. 18-33, DOI: 10.1016/j.esr.2019.04.014, 2019.

[31] M. S. Husami, Energy Crisis in Kurdistan and the Impact of Renewable Energy, 2007.

[32] S. Al-Din, D. Kuzovic, M. Iranfar, "Renewable Energy Strategies to Overcome Power Shortage in Kurdistan Region of Iraq," Industrija, vol. 45 no. 2,DOI: 10.5937/industrija45-12770, 2017.

[33] R. Vaziri, "Solar Energy in Kurdistan, Iraq: A Comprehensive Review of Economic and Environmental Implications, Policies, and Advancements," International Journal of Kurdish Studies, vol. 10 no. 1, pp. 47-74, 2024.

[34] ESRI, "Understanding Euclidean Distance Analysis," ESRI GIS Dictionary, 2018.

[35] P.-E. Danielsson, "Euclidean Distance Mapping," Computer Graphics and Image Processing, vol. 14 no. 3, pp. 227-248, DOI: 10.1016/0146-664X(80)90054-4, 1980.

[36] J. Babbar, N. Rathee, "Satellite Image Analysis: A Review," .

[37] D. Sowmya, P. D. Shenoy, P. Deepa, K. Venugopal, "Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey," International Journal of Computer Applications, vol. 161 no. 11, pp. 24-37, DOI: 10.5120/ijca2017913306, 2017.

[38] A. Vanjare, S. N. Omkar, J. Senthilnath, "Satellite Image Processing for Land use and Land Cover Mapping," International Journal of Image, Graphics and Signal Processing, vol. 6 no. 10, pp. 18-28, DOI: 10.5815/ijigsp.2014.10.03, 2014.

[39] L. Khodakarami, A. Soffianian, N. Mirghafari, M. Afyuni, A. Golshahi, "Concentration Zoning of Chromium, Cobalt and Nickel in the Soils of Three Sub-Basin of the Hamadan Province Using GIS Technology and the Geostatistics," JWSS - Journal of Water and Soil Science, vol. 15 no. 58, pp. 243-254, 2012.

[40] L. Khodakarami, A. Soffianian, T. E. Mohamadi, N. Mirghafari, "Study of Heavy Metals Concentration Copper, Zinc and Arsenic Soil Using GIS and RS Techniques (Case Study: Kaboudarahang, Razan and Khonjin-Talkhab Catchment in Hamedan Province)," 2011.

[41] A. Mousavi, N. Safyaniyan, A. Mirghafari, L. Khodakarami, "Investigate the Spatial Distribution of Heavy Metals in Soils of Hamedan Province," Journal of Soil Science, Soil and Water, A, vol. 25 no. 4, 2011.

[42] A. R. Soffianian, H. B. Bakir, L. Khodakarami, "Evaluation of Heavy Metals Concentration in Soil Using GIS, RS and Geostatistics," IOSR, vol. 9 no. 12, pp. 61-75, 2015.

[43] A. E. Dinçer, A. Demir, K. Yılmaz, "Enhancing Wind Turbine Site Selection Through a Novel Wake Penalty Criterion," Energy, vol. 283,DOI: 10.1016/j.energy.2023.129096, 2023.

[44] I. Kamdar, S. Ali, J. Taweekun, H. M. Ali, "Wind Farm Site Selection Using WAsP Tool for Application in the Tropical Region," Sustainability, vol. 13 no. 24,DOI: 10.3390/su132413718, 2021.

[45] J. Feng, L. Feng, J. Wang, C. W. King, "Evaluation of the Onshore Wind Energy Potential in Mainland China—Based on GIS Modeling and EROI Analysis," Resources, Conservation and Recycling, vol. 152,DOI: 10.1016/j.resconrec.2019.104484, 2020.

[46] D.-i. Ā.-i Islāmī, International Journal of Energy and Environmental Engineering, 2010.

[47] S. S. Yildiz, "Determining Wind Energy Potential Using Geographic Information System Functions: A Case Study in Balıkesir, Turkey," Applied Sciences, vol. 13 no. 16,DOI: 10.3390/app13169183, 2023.

[48] T. S. M. Cunden, J. Doorga, M. R. Lollchund, S. D. D. V. Rughooputh, "Multi-Level Constraints Wind Farms Siting for a Complex Terrain in a Tropical Region Using MCDM Approach Coupled With GIS," Energy, vol. 211,DOI: 10.1016/j.energy.2020.118533, 2020.

[49] M. Taoufik, A. Fekri, "GIS-Based Multi-Criteria Analysis of Offshore Wind Farm Development in Morocco," Energy Conversion and Management X, vol. 11, 2021.

[50] B. Sliz-Szkliniarz, J. Vogt, "GIS-Based Approach for the Evaluation of Wind Energy Potential: A Case Study for the Kujawsko-Pomorskie Voivodeship," Renewable and Sustainable Energy Reviews, vol. 15 no. 3, pp. 1696-1707, DOI: 10.1016/j.rser.2010.11.045, 2011.

[51] G. Ren, J. Wan, J. Liu, D. Yu, "Assessing Temporal Variability of Wind Resources in China and the Spatial Correlation of Wind Power in the Selected Regions," Journal of Renewable and Sustainable Energy, vol. 12 no. 1,DOI: 10.1063/1.5129290, 2020.

[52] M. Kanoglu, Y. Cengel, J. Cimbala, "Fundamentals and Applications of Renewable Energy," TIDEE: TERI Information Digest on Energy and Environment, vol. 20 no. 2, pp. 278-278, 2021.

[53] N. Gupta, "A Review on the Inclusion of Wind Generation in Power System Studies," Renewable and Sustainable Energy Reviews, vol. 59, pp. 530-543, DOI: 10.1016/j.rser.2016.01.009, 2016.

[54] T. L. Saaty, "The Analytic Hierarchy Process (AHP)," The Journal of the Operational Research Society, vol. 41 no. 11, pp. 1073-1076, 1980.

[55] G.-H. Tzeng, J.-J. Huang, Multiple Attribute Decision Making: Methods and Applications, 2011.

[56] J. Malczewski, GIS and Multicriteria Decision Analysis, 1999.

[57] W. Zhang, Computational Ecology: Graphs, Networks and Agent-Based Modeling, 2012.

[58] J. Ma, N. R. Scott, S. D. DeGloria, A. J. Lembo, "Siting Analysis of Farm-Based Centralized Anaerobic Digester Systems for Distributed Generation Using GIS," Biomass and Bioenergy, vol. 28 no. 6, pp. 591-600, DOI: 10.1016/j.biombioe.2004.12.003, 2005.

[59] R. Mitra, J. Das, "A Comparative Assessment of Flood Susceptibility Modelling of GIS-Based TOPSIS, VIKOR, and EDAS Techniques in the Sub-Himalayan Foothills Region of Eastern India," Environmental Science and Pollution Research, vol. 30 no. 6, pp. 16036-16067, DOI: 10.1007/s11356-022-23168-5, 2023.

[60] K. Khosravi, M. Sartaj, M. Karimi, J. Levison, A. Lotfi, "A GIS-Based Groundwater Pollution Potential Using DRASTIC, Modified DRASTIC, and Bivariate Statistical Models," Environmental Science and Pollution Research, vol. 28 no. 36, pp. 50525-50541, DOI: 10.1007/s11356-021-13706-y, 2021.

[61] O. O. E. E. R. ENERGY, "Wind Energy Basics," 2019. https://www.energy.gov/eere/wind/wind-energy-basics

[62] M. Kanoğlu, Y. A. Çengel, J. M. Cimbala, Fundamentals and Applications of Renewable Energy, 2020.

[63] L. Khodakarami, S. Pourmanafi, Z. Mokhtari, A. R. Soffianian, A. Lotfi, "Urban Sustainability Assessment at the Neighborhood Scale: Integrating Spatial Modellings and Multi-Criteria Decision Making Approaches," Sustainable Cities and Society, vol. 97,DOI: 10.1016/j.scs.2023.104725, 2023.

[64] L. Khodakarami, "Spatial Modeling of Micro-Scale Carbon Dioxide Sources and Sinks in Urban Environments: A Novel Approach to Quantify Urban Impacts on Global Warming," Greenhouse Gases: Science and Technology, vol. 14 no. 3, pp. 470-491, 2024.

[65] L. Khodakarami, S. Pourmanafi, A. R. Soffianian, A. Lotfi, "Modeling Spatial Distribution of Carbon Sequestration, CO 2 Absorption, and O 2 Production in an Urban Area: Integrating Ground-Based Data, Remote Sensing Technique, and GWR Model," Earth and Space Science, vol. 9 no. 7,DOI: 10.1029/2022EA002261, 2022.