A Geographic Information System-Based Model and Analytic Hierarchy Process for Wind Farm Site Selection in the Red Sea

Abstract

The wind is one of the most important sources of renewable energy. However, it is associated with many challenges, with one of the most notable being determining suitable locations for wind power farms based on different evaluation criteria. In this study, we investigated the suitability of wind farm sites in the Red Sea off the coast of Saudi Arabia using the analytical hierarchy process (AHP) and a Geographic Information System (GIS). We assessed the suitability of offshore locations for wind energy projects, differentiating between fixed and floating turbines, and identified a 4180 km² area as less suitable, whereas the 33,094 km², 20,618 km², and 11,077 km² areas were deemed suitable, very suitable, and extremely suitable, respectively. These findings highlight the differences in suitability levels based on specific geographical features. Moreover, the extremely suitable location, which has the largest area of 3032 km², has the capacity to generate an annual energy output of 56,965,410 MWh/year.

1. Introduction

Our planet is facing critical threats due to a combination of environmental pollution and global warming [1]. A large amount of CO₂ has been—and continues to be—released into the atmosphere during the combustion of fossil fuels for energy generation [2,3]. Fortunately, the increasingly widespread adoption of renewable energy sources offers a practical solution to the climate crisis.

Renewable energy is characterized by its safety and sustainability. Wind energy is a prime example, as it is a safe, clean, and substantial renewable source. Saudi Arabia is a promising site for wind energy projects, with an abundance of wind resources, and has attracted researchers’ interest in terms of its energy potential. The field of renewable energy in Saudi Arabia has recently expanded, and there is a plan to install 57.5 GW of renewable energy sources by 2030 to meet the national goal of 9.5 GW of wind energy [4]. This demonstrates the importance of assessing the possible wind resources in the region. However, wind energy projects have high installation costs; as such, it is necessary to determine the amount of potential wind energy that can be obtained at different locations prior to installation. This can help to reduce the cost of the operation stage [5].

Numerous researchers have assessed the capacity for onshore wind energy generation in Saudi Arabia [6,7,8,9]. However, the spatial sites for offshore wind energy in Saudi Arabia, which has two coastlines that expand along the western and eastern regions—the Red Sea coastline, which is approximately 1760 km long, and the Arabian Gulf coastline, which encompasses around 560 km—have yet to be assessed [10]. These coastlines are promising and should be considered as potential sites for offshore wind energy infrastructure [11]. Accordingly, we aimed to evaluate the most suitable sites for wind energy projects in Saudi Arabia. Identifying a suitable location is one of the most important steps in wind project development. Therefore, studies should be conducted to assess the amount of wind energy that can be accessed at different locations prior to the conception of wind energy projects. When selecting an optimal location, common evaluation criteria such as wind speed, water depth, wind power density, distance from power lines, and distance from the shore should be considered [12].

There is a demonstrable need to determine suitable locations for offshore wind farms in Saudi Arabia. This study offers the following contributions:

• This is the first study to investigate suitable locations for floating turbines in the Red Sea in Saudi Arabia.
• We utilized a GIS-based MCDM model using the AHP technique [13] to weigh all the required criteria for such projects.
• We provide a list of suitable locations for wind farms, along with their coordinates, total areas and projected energy production.

The paper is structured as follows: in Section 2, we present and discuss relevant works in the field of renewable energy generation. In Section 3, we describe the steps used to build the proposed model. In Section 4, we present the findings of the study, including a list of prospective locations for wind farms in the Red Sea that are classified according to their suitability. The conclusions, future research directions, and recommendations of this study are presented in Section 5.

2. Related Works

Some researchers have examined the suitability of offshore wind energy project sites in various countries. However, there is still a lack of research conducted at global sites [14]. According to the National Renewable Energy Laboratory (NREL), a study was conducted to assess offshore wind energy in the United States [15] based on the wind speed, water depth, and distance from the shore. Only areas with wind speeds greater than 7 m/s were eligible for inclusion in the study. Areas with water deeper than 60 m were deemed suitable for floating turbines as opposed to fixed structures. The distance from the shoreline was also measured, and only the areas with a maximum of 50 nm were considered. The data were analyzed and suitability maps were created. However, no information was provided about the model used for analysis.

In 2018, Mahdy and Bahaj assessed the offshore wind resources in the Red Sea region and the Mediterranean Sea around Egypt. Spatial assessment was performed via GIS-based multi-criteria decision analysis (MCDA), and AHP was used to give weight to each criterion. The evaluation criteria considered were wind power density, water depth, distance from the shoreline, soil properties, and distance from the National Grid. Shipping routes, ports, military zones, natural parks, cables and pipelines, fishing areas, and oil and gas extraction areas were deemed problematic and considered grounds for exclusion. Different evaluation and constraints criteria were aggregated using GIS tools and the WLC (Weighted Linear Combination) aggregation method. The WLC integrates various elements by multiplying each factor’s value by its corresponding significance (weight) and aggregating these weighted values to yield a final suitability score. Three suitable sites were identified: two in the Egyptian territorial waters and a third between Egypt and Saudi Arabia [14].

The AHP approach was also implemented by Bahrain [16] to find the optimal locations for offshore wind projects. The evaluation criteria were wind speed, wind depth, and distance from the shoreline, in addition to different constraints. Bahrain’s territorial seas comprise 10 wind farm locations suitable for wind-energy production. These areas cover approximately 4% of the marine area. The wind farms can generate 2.68 TWh/year of wind energy, which is equivalent to over 10% of the Kingdom’s annual power consumption.

In 2020, Díaz and Soares sought to identify suitable sites for floating wind farms using the GIS approach on the Atlantic continental European coastline [17]. First, unsuitable locations were excluded based on exclusion criteria such as military activity, environmentally protected areas, underwater lines and pipelines, etc. Then, different criteria were used to determine the level of suitability based on various categories: metocean data, such as wind velocity, wind potential, water depth, temperature, etc.; logistics, such as distance from the local electrical grid, proximity to various locations such as the shoreline, residential areas, maritime routes, underwater lines, and the airport; and marine environment and techno-economic data. After the exclusion criteria were applied, 0.2% of the total area was selected, and the evaluation process was performed. The generation capacity of wind resources was projected to exceed 32 GW, proving that the Atlantic coast holds promising potential for wind resource generation.

In 2021, another study conducted an the offshore assessment of wind energy in Morocco [18]. This study used the GIS-based multi-criteria decision-making (MCDM) methodology, which starts with the exclusion of restricted areas. They used different evaluation criteria, including socio-economic, environmental, and technical criteria, such as tourism, protected areas, migratory birds’ routes, distance from the shoreline, water depth, distance from power grids, distance from ports, distance from airports, submarine cables, shipping routes, and distance from Blue Flag Beaches. Moroccan marine military data were unavailable. The fuzzy analytic hierarchy process (Fuzzy-AHP) was implemented to obtain the weight for each parameter and to generate a suitability map that comprised five categories: unsuitable, less suitable, suitable, very suitable, and extremely suitable. Al Jadida, Agadir, Tan, and Dakhla were identified as the most suitable sites. Wind speed was the most important criterion, with a weight of 39%; the water depth weight was 21%, while proximity to the power grid was weighted at 13%.

In 2020 [19], an intuitionistic fuzzy MCDM with the TOPSIS method was proposed to facilitate offshore wind farm site selection in the Black Sea near Turkey. Economic, technical, social, and environmental factors, as well as the investment cost, total project payback period, and electricity cable installation cost were taken into consideration.

Wang et al. [20] used a combination of the spherical fuzzy analytical hierarchy process (SF-AHP) and weighted aggregated sum product assessment (WASPAS) to test the effectiveness of the proposed method in a case study in Vietnam. The authors presented a summary of criteria from the related works, and wind speed, water depth, distance from the shore, and distance from the electricity grid were deemed the most significant criteria. However, this paper did not use GIS systems to locate candidate sites.

The AHP has been utilized to solve location problems across a diverse range of fields, such as determining optimal hospital locations in Malaysia [21]. It has also proven valuable in renewable energy applications, specifically for identifying suitable solar farm sites [5,22], and has been utilized in urban planning to optimize the placement of electric vehicle charging stations [23] and to identify appropriate areas for residential development [24].

As shown in

Table 1. Wind site suitability-related studies.

Ref.	Study Area Location	Applied Method	Evaluation Criteria
[15]	USA	Not defined	Wind speed, water depth, and distance from shore.
[14]	Egypt	AHP	Water power density, water depth, distance from shoreline, soil properties, and distance from the National Grid.
[16]	Bahrain	AHP	Wind speed, water depth, and distance from coastlines.
[17]	Atlantic continental European coastline	Not defined	Wind speed, wind potential, water depth, distance from power grid, distance from shore, wave condition, temperature, etc.
[18]	Morocco	FAHP	Wind speed, water depth, distance from power grid, distance from shore, ports, etc.
[19]	Black Sea region of Turkey	Intuitionistic fuzzy + TOPSIS	Wind speed, water depth, distance from ports, distance from shore, distance from restricted areas, cost, etc.
[20]	Vietnam	SF-AHP WASPAS	Wind resources, environmental impact, construction and maintenance conditions, societal impact, conditions onshore, and economic impact were considered.
[25]	Saudi Arabia	Boolean Mask model	Wind speed, water depth, distance from shore, distance from grid, shipping routes, cables, and restricted areas.

, there is a lack of offshore wind power farm (OWPF) site selection studies conducted in Saudi Arabia. In fact, thus far, only one such study has been conducted in the Red Sea [25], and it used the Boolean Mask Model. Therefore, to the authors’ knowledge, this paper documents the first use of the GIS-AHP approach to OWPF in Saudi Arabia.

3. Materials and Methods

In this section, we describe the methodology applied to conduct a geospatial analysis of wind energy in the Red Sea. The GIS-AHP model was built to perform the site stability analysis. The main steps were as follows:

• Defining the constraints and evaluation criteria used for site suitability.
• Collecting data related to the criteria.
• Conducting spatial analysis using several techniques such as geometric and distance operations.
• Designing the Spatial Decision Support system (SDSS) using GIS-AHP.
• Providing and discussing the results, which outline the most promising potential wind energy generation sites in the Red Sea.

3.1. Study Area

The Red Sea, sometimes referred to as Bahr al Ahmar in Arabic, has an elongated shape and is around 2000 km in length, reaching a maximum width of 355 km and covering a surface area of approximately 458,620 km². The Red Sea can be divided into three separate depth zones, comprising shallow shelves with depths of less than 50 m, deep shelves ranging from 500 to 1000 m, and a central axis with depths ranging from 1000 to 2900 m. An estimated 25% of the Red Sea’s area is characterized by depths of less than 50 m, while around 40% is shallow, reaching depths of less than 100 m [26]. Figure 1 depicts the study area of this paper: the Red Sea in Saudi Arabia.

3.2. Defining Criteria

One of the most important aspects of site suitability analysis is identifying factors or criteria for evaluating suitable locations. The evaluation criteria should comprise factors that directly affect the cost and performance of wind power plants. During the planning process, we identified two categories of criteria: evaluation criteria, which are used in the site suitability analysis, and constraints criteria, which are used to eliminate unsuitable areas. Evaluation criteria and constraints were chosen using the design principles developed by the National Renewable Energy Laboratory (NREL) and in previous studies [14,15,25,27,28,29]. The common criteria for offshore wind farms are wind speed, water depth, and proximity to the electricity grid and protected areas [12]. However, the criteria differ slightly depending on the study area, data availability, and the perspective of the researcher(s) [5].

The evaluation criteria used in this study were the wind speed (C1), wind power density (C2), and distance from the electrical grid (C3); the constraints criteria were water depth and distance from the shore, shipping lanes, protected areas, and cables, as shown in

Table 2. Evaluation criteria.

	Criteria	Objectives
C1	Annual average wind speed at 100 m height AGL	Maximize
C2	Wind power density	Maximize
C3	Distance from power lines	Minimize

and

Table 3. Constraints.

Exclusion Criteria	Unsuitable Areas
Water depth	Fixed turbine        <5 or >60 m Floating turbine      <60 or >1000 m
Distance from shore	<1.5 or >100 km
Shipping Lanes	All
Underwater Cables	All
Protected areas	All

.

3.3. Data Collection

This study included eight different data layers from several sources provided in

Table 4. Criteria data and their sources.

Data Layer	Type of Criterion	File Format	Source
Wind speed	Weighted	Raster	Global Wind Atlas [30]
Wind power density	Weighted	Raster	Global Wind Atlas [30]
Electricity power lines	Weighted	Vector	NextGIS [31]
Shoreline	Constraint	Vector	NextGIS [31]
Water depth	Constraint	Raster	GEBCO Gridded Bathymetry Data [32] Global Wind Atlas [30]
Shipping routes	Constraint	Vector	ArcGIS [33]
Protected areas	Constraint	Vector	Protected planet [34]
Underwater cables	Constraint	Vector	ArcGIS [33]

. The average wind speed and wind power density data were obtained from the Global Wind Atlas portal [30]. Figure 2a displays the yearly average wind speed in South Africa at 100 m above ground level (AGL). The study region has a minimum wind speed of 1.1 m/s and a maximum wind speed of 12.75 m/s. Regarding the wind power density, the minimum value in the Red Sea is 36 W/m² and the maximum value is 2028.97 W/m², as shown in Figure 2b. The power lines and the Red Sea’s coastline are depicted in Figure 2c,d. Figure 2e shows the water depth of the Red Sea, and most of the study area achieves a depth greater than 60 m. In addition, Figure 2f–h show the data related to the protected areas, underwater cables, and shipping route.

3.4. Spatial Analysis

3.4.1. Creating the Constraints Map

The constraints map is a geographical representation of the study area, showing both suitable and unsuitable sites for wind farms. The purpose of this map is to define the regions that should be excluded from the appropriateness examination. Figure 3 provides a flowchart which can be used to generate such a map.

The first step involved projecting all the constraints data layers onto the WGS 1984 Web Mercator Auxiliary Sphere projection system to obtain the measurements in meters. Furthermore, the water depth layer was reclassified to assign a value of 1 to the suitable areas and 0 to the unsuitable areas. It was necessary to know which types of turbines to use in order to determine the suitable areas based on the water depth. Fixed turbines are most suitable for water depths between 5 m and 60 m, whereas floating wind turbines are typically used at depths between 60 m and 1000 m [14,19]. Therefore, the water depth layer was reclassified and converted to a binary according to the type of turbines, as shown in Figure 4a,b.

For the Red Sea coastline layer, the Euclidean distance was determined to compute the distance from the shore, and then the layer was reclassified and converted to 1 and 0 according to distance. As shown in Figure 4c, the suitable regions between 1.5 km and 100 km from the shore are depicted in green. Locations that are far away from the shoreline were considered inappropriate due to the increased cable costs [14].

For the shipping route, protected areas, and underwater cables, a feature-to-raster tool was used to transform the data into raster format. Then, all datasets, including constraints, were converted into binary format, presenting raster values of 0 and 1. A value of 0 represents unsuitable areas, while a value of 1 represents suitable areas. To obtain the constraints map shown in Figure 4c,d, all the binary layers were multiplied using the Boolean OR process. There were two constraint maps that differed based on the turbine types.

3.4.2. Creating the Criteria Weighted Map

In order to generate the weighted map via the AHP model, the evaluation criteria data need to be processed using spatial analysis techniques, as shown in Figure 5.

The average wind speed (C1) and wind power density (C2) dataset were acquired from the Global Wind Atlas. The map was restricted to the boundaries of the study region to obtain only the offshore wind speed data, which included both onshore and offshore wind speeds.

A dataset focusing on the distance from power lines (C3) was obtained from NextGIS. Locations at significant distances from electricity networks are inappropriate and lack economic feasibility [35]. The three vector layers were adjusted to facilitate the use of the WGS 1984 Web Mercator Auxiliary Sphere projection system to convert the measurement from degrees to meters. After that, the Euclidean distance calculator was used to calculate the distance in kilometers between different locations inside the designated study region and the electrical power lines.

Given that each of these criteria contains a distinct range of values, it was necessary to establish a standardized scale in order to integrate them into a unified layer. Consequently, the criteria were categorized into four distinct classes denoted by new raster values ranging from 0 to 4. A rating of 4 signified the highest level of suitability, while a rating of 1 indicated the lowest level of suitability and 0 was used to indicate unsuitable areas. The reclassify tool within the spatial analyst toolset offered by ArcGIS was used to conduct the reclassification.

The wind speed was separated into five classes: unsuitable, less suitable, suitable, very suitable, and extremely suitable, based on the wind power class established by the National Renewable Energy Laboratory (NREL) [18,36,37,38]. The reclassification technique involved manual intervals. Moreover, the wind power density values were reclassified according to the NREL [39,40], as shown in

Table 5. Reclassification of weighted criteria map.

Category	Distance (m) to	Wind Speed (m/s)	Wind Power Density (%)	Score	Suitability
	Power Lines
A	>200	<4	<200	0	Not suitable
B	150–200	4–5	200–300	1	Less suitable
C	100–150	5–6	300–400	2	Suitable
D	50–100	6–7	400–500	3	Very suitable
E	0–50	>7	>500	4	Extremely suitable

.

As shown in Figure 6a, the wind speed of the Red Sea exhibited notable variations across several geographical areas. In the southern regions, the average wind speeds primarily fell within the range of 4 to 6 m/s. As we moved into the middle region, a significant change took place, and the average wind speeds ranged from 6 to 7 m/s. As we moved towards the north, the wind speeds increased, with most of them being between 7 and 12.75 m/s. These particular regions are notable for their high winds, which provide significant opportunities for energy generation and for the implementation of wind energy projects with enhanced efficiency. In general, the wind speeds in the Red Sea vary along the north–south gradient. The central and northern areas are particularly suitable for wind energy development because they have higher wind speeds. Wind speeds of 4 m/s and less are considered unsuitable for offshore wind projects.

The investigation of wind power density in the Red Sea revealed different values of energy potential among several geographical areas, as shown in Figure 6b. In the southern region, the wind power density values primarily fell between 36 and 300 W/m². When moving towards the central region, noticeable differences became apparent, as a specific part of the region showed wind power density levels ranging from 300 to 400 W/m².

In the northern region of the Red Sea, a particular location displayed values ranging from 401 to 500 W/m², which was indicative of heightened energy potential. Furthermore, two specific regions possessed outstanding energy resources, demonstrating exceptionally high wind power density ranging from 501 to 2028 W/m². The distances from power lines according to the new classification values are shown in Figure 6c. The optimal location for offshore wind farms is in close proximity to pre-existing power infrastructure, which facilitates a reduction in cabling costs and a decrease in the complexity of installation and maintenance processes [18].

3.5. Spatial Decision Support

It is necessary to design a model to identify suitable sites for wind farms. The model will evaluate and prioritize multiple conflicting criteria to support decision-making. AHP is the preferred approach, providing accurate results, especially with regard to site suitability analysis for wind energy projects. Therefore, in this study, we selected the AHP algorithm, as it is a robust and flexible MCDM. It allows decisions to be structured hierarchically to reduce complexity and highlight relationships between criteria. It can mix qualitative and quantitative criteria in the same decision framework [41]. The basic theory of AHP can be described as follows: we assume that we have obtained different independent criteria C1; C2; ⋯; and Cn, and that they have weights w1, w2, etc. The decision-maker does not know the values of these weights, but the different criteria can be compared pairwise. Pairwise comparisons are used as the fundamental basis of the analytic hierarchy process (AHP) to identify the relative significance of each of criteria [5,41]. Pairwise comparison is a systematic process in which criteria are compared two at a time to determine their relative importance.

The first of this algorithm’s four fundamental ideas is organizing the difficult decision problem into a hierarchy of criteria. Second, Saaty’s 1 to 9 scale assessments are used to construct pairwise comparisons of criteria at each level of the hierarchy, as indicated in

Table 6. The fundamental scale according to Saaty [42].

Intensity of Importance	Definition	Explanation
1	Equally important	The objective is equally supported by two activities.
3	Moderate importance of one over another	One activity marginally outweighs another in favor of experience and judgment.
5	Essential importance	One activity is greatly preferred over another by experience and judgment.
7	Extreme importance	An activity is strongly preferred, and its domination is seen in action.
9	Absolute importance	A specific activity is supported by the strongest possible evidence.
2, 4, 6, 8	Intermediate values	Compromising when necessary.

[42]. Third, the decisions at various levels are added vertically. The last phase constitutes verifying the judgments’ consistency [5,34,35].

The AHP provides quantitative techniques for detecting judgmental inconsistencies. The consistency ratio (CR) can be calculated according to the reciprocal matrices. The maximum value in a reciprocal matrix, ${\lambda }_{max}$, is always greater than the number of rows or columns, $n$. If there are no contradictions in a pairwise comparison, ${\lambda }_{max}$ = $n$. A consistency index (CI) represents the inconsistency of pairwise comparisons [42]. It can be calculated as follows:

$$\mathrm{C}\mathrm{I}={\displaystyle \frac{{\mathsf{\lambda }}_{\mathrm{m}\mathrm{a}\mathrm{x}}-\mathrm{n}}{\mathrm{n}-1}}$$

(1)

Later, the consistency ratio can be used to calculate the pairwise comparisons’ coherence (CR):

$$\mathrm{C}\mathrm{R}={\displaystyle \frac{\mathrm{C}\mathrm{I}}{\mathrm{R}\mathrm{I}}}$$

(2)

This ratio is determined by dividing the CI by an index selected at random from Saaty’s suggested random matrix [42]. He provided random index (RI) values depending on various matrix sizes $n$, as shown in

Table 7. Random index values, according to Saaty and Tran [42].

$n$	1	2	3	4	5	6	7	8	9	10
$RI$	0.00	0.00	0.58	0.90	1.12	1.24	1.32	1.41	1.45	1.49

.

Finally, if the CR value is equal to or smaller than the conventional value of 0.10, it can be inferred that the matrix is consistent. If the CR is greater than 0.1, the pairwise comparisons must be repeated. If the CR obtained is still greater than 0.1, the hierarchy tree and the criteria should be reconsidered [42]. We used data from previous research to obtain the pairwise comparison values [35,36,43,44]. The opinions of specialists in the field of wind energy development and related literature were also considered. The relative importance of each of the three chosen criteria is determined by comparing them in pairs.

The weighting relationships of the proposed AHP framework are mainly based on established industry standards and peer-reviewed literature. The proposed approach is primarily based on the Wind Resource Assessment Handbook developed by the National Renewable Energy Laboratory (NREL), which establishes that wind farm sites’ suitability is dependent on two equally weighted parameters: wind speed and wind power density. These parameters are classified based on the specific ranges of both metrics. This study assigns equal weights to wind speed and wind power density [27]. In addition, offshore wind farm site selection analyses utilize standardized datasets specific to each potential location, with particular emphasis on key characteristics such as wind speed and wind power density. These fundamental parameters serve as primary determinants during the site assessment process [45]. Furthermore, the validity of prioritizing wind characteristics is further supported by a comprehensive systematic review of offshore wind farm siting studies [29]. This review analyzed the frequency and importance of various siting criteria across multiple research contributions. Wind speed emerged as the most frequently considered factor, closely followed by power density. The review’s findings validate our approach by demonstrating the importance of wind-related parameters in site selection. Additionally, a site selection study for offshore wind farms in Morocco provides quantitative validation for our weighting scheme. Their expert-derived AHP matrices assigned wind speed five times greater importance than grid proximity, with proximity to power grid receiving a weight of 13%. The remarkable alignment between their expert judgments and proposed weightings provides additional validation for proposed approach. Based on evidence from multiple sources, the proposed AHP pairwise comparison matrix assigns equal weights to wind speed and wind power density, reflecting the NREL standard. It also presents a 5:1 importance ratio between wind parameters and grid proximity, consistent with expert judgments from comparable studies and relative weightings that align with empirically validated relationships from peer-reviewed literature. This weighting scheme thus represents a combination of established industry standards, systematic review findings, and expert-validated relationships from comparable site selection studies.

The criteria weights are determined by solving the decision matrix shown in

Table 8. The normalized pairwise comparison matrix and criteria weights.

Criteria	C1	C2	C3	Weights	Weights %
C1	1	1	5	0.455	45.5%
C2	1	1	5	0.455	45.5%
C3	1/5	1/5	1	0.091	9%
				Inconsistency ratio = 0

. The approximate method [13] is used to obtain the weights; this requires the normalization of the comparison matrix. To calculate the weights, the values in each column in Table 8 are added together, comprising a total of 2.2 for the first and second column and 11 for the third column. Next, each cell is divided by the total of the column to obtain the normalized values for each cell. The average weight of each row is calculated to obtain the final weight for each criterion. Table 8 presents the normalized matrix, including the weights assigned to each of the investigated criteria.

The CR is 0, a value indicating perfect consistency. According to the results, wind speed and wind power density each have a weight of 45.5%, which has the greatest impact on the final result, followed by distance from power lines, with 9% weight.

3.6. Wind Suitability Map

To obtain the weighted overlay map, as shown in Figure 7, each layer of the evaluation criteria is multiplied by the score of the location, which represents the class number (0,1,2,3,4). The weighted overlay function is used as shown below:

$$S_l=\sum _{i=1}^n{w}_i{x}_i$$

(3)

where S_l represents the suitability index and n indicates the three evaluation criteria: wind speed, wind power density, and distance from power lines. w_i represents the calculated weight of criteria i shown in Table 8, while the score of location $l$ is represented by x_i.

To obtain the final wind suitability map, as shown in Figure 8, it is necessary to consider the AHP weighted map in Figure 7 and the constraint maps in Figure 4d,e. The AHP aggregated weighted map is multiplied by the constraints map represented by two values (0 for the unsuitable regions and 1 for suitable regions). The resulting map will be reclassified as “extremely suitable”, “very suitable”, “suitable”, “less suitable”, and “unsuitable”, with class 4 being the most suitable and class 1 being the least suitable. The suitability index (SI) of the different locations in the study area is determined as follows:

$${SI}_l=B_l{S}_l$$

(4)

where SI_l represents the suitability index of location l; B_l represents the binary score of location l in the constraints map; and S_l represents the suitability index of location l. Furthermore, Figure 8b shows two main regions categorized as class 4, both of which have a high level of suitability for the installation of floating turbines. The underwater cables and main shipping route divide these areas, which exist in the north and middle of the Red Sea, into multiple areas. The total areas of candidate sites suitable for floating turbines exceed those that are suitable for fixed turbines, as shown in Figure 8a.

4. Results and Discussion

4.1. Wind Farm Location Suitability Map for the Red Sea

This paper aimed to find suitable locations for offshore wind energy generation in Saudi Arabia, with particular focus on the Red Sea. Several spatial processing approaches were applied to reclassify the three evaluation criteria (wind speed, wind power density, and power lines) to a range of 0 to 4, where 0 indicates unsuitability, 1 indicates the lowest level of suitability, and 4 represents the highest level of suitability. Subsequently, all reclassified criteria were combined with their respective weights. The weights were determined using the MCDM approach and the AHP technique. The experiments were conducted utilizing the ArcGIS Pro 2.5.0 software. The criteria for constraints, namely water depth, distance from the shore, main shipping routes, protected areas, and underwater cables, were converted into binary representations, in which a value of 0 indicated that the site was unfit for wind energy generation purposes, while a value of 1 represented a suitable location. To identify the most suitable wind farm locations from the research area map, the weighted and constraints criterion maps were merged by combining their suitability index with their binary scores. Figure 8a,b show the resulting maps, while Figure 9 and Figure 10 illustrate the total geographical area covered by the different suitability classes. The overall area available for constructing floating turbines is considerably larger than the total area that is suitable for installing fixed turbines, largely due to the fact that fixed turbines can only be installed at depths between 5 and 60 m. On the other hand, floating turbines have a wider range of permissible water depths, ranging from 60 to 1000 m.

The present study evaluated the suitability of offshore sites for wind energy projects, depending on the type of turbine: floating or fixed. For the floating turbines, a significant area of 109,594 km² was identified as unsuitable for wind energy projects. On the other hand, a total area of 4180 km² was determined to be less suitable for the deployment of floating turbine systems. An area measuring 33,094 km² was considered to be suitable for installing floating turbines. A significant land area measuring 20,618 km² was classified as highly suitable. In contrast, a total area of 11,077 km² was determined to be extremely suitable for the deployment of floating turbines.

For the fixed turbines, a total area of 142,577 km² was determined to be unsuitable, while a 16,640 km² area was identified as less suitable, 7498 km² was deemed suitable, 2391 km² was found to be very suitable, and 1007 km² was classified as extremely suitable. The regions classified as extremely and very suitable locations for floating turbines significantly exceeded the areas designated for fixed turbines.

In

Table 9. Details of the top six offshore wind farm sites in the Red Sea in Saudi Arabia (floating turbines).

Location	Suitability Score	Area (km2)	Longitude	Latitude
A	4	3031.887	35°00′12.99435118″ E	27°24′35.59010080″ N
B	4	2166.873	37°42′23.53242321″ E	24°03′09.76752866″ N
C	4	2157.723	37°42′52.49924909″ E	23°45′10.30069639″ N
D	4	1425.097	37°15′27.50454492″ E	23°53′10.85928514″ N
E	4	657.1447	35°23′44.85390988″ E	26°41′57.32080238″ N
F	4	597.0511	34°47′21.39908665″ E	27°49′38.44687506″ N

and

Table 10. Details of top 4 offshore wind farm sites in the Red Sea, Saudi Arabia (fixed turbines).

Location	Suitability Score	Area (km2)	Longitude	Latitude
A	4	141.489	37°15′52.96970088″ E	24°30′02.29247796″ N
B	4	122.2286	37°41′53.22453351″ E	24°09′58.98889420″ N
C	4	54.94649	38°01′36.67413598″ E	23°58′05.35774664″ N
D	4	46.63784	34°38′20.51304624″ E	27°56′26.96018920″ N

, the most suitable locations for floating and fixed turbines are presented in km² and their exact coordinates are provided. As shown in Table 9, location A achieved the greatest total area of 3032 km². Figure 11 depicts the top six most suitable sites.

Table 10 displays the top four class 4 sites that are most suitable for the installation of fixed turbines, along with their respective areas and coordination. As mentioned previously, the areas in km² are significantly smaller compared with the candidate sites for floating turbines. A map of these areas can be seen in Figure 12.

4.2. Model Validation

To validate the accuracy of the suggested areas in Table 9 and Table 10, our evaluation criteria and constraint data are analyzed and discussed, as shown in

Table 11. Information on the top six offshore wind farm sites in the Red Sea, Saudi Arabia (floating turbines).

Location	Wind Speed (m/s)	Wind Power Density (W/m2)	Water Depth (m)	Distance to Powerlines (m)	Distance to Coastline (m)	Weight
A	8.2	596.95	−847	52,094	52.629	3.87
B	8.0	575.05	−629	28,203	16.7549	3.96
C	7.5	460.42	−554	53,425	41.9155	3.42
D	7.5	416.16	−619	63,065	46.4981	3.42
E	7.5	455.86	−823	6840	58.0909	3.42
F	9.5	996.19	−669	24,663	23.695	3.96

and

Table 12. Information on the top six offshore wind farm sites in the Red Sea, Saudi Arabia (fixed turbines).

Location	Wind Speed (m/s)	Wind Power Density (W/m2)	Water Depth (m)	Distance to Powerlines (m)	Distance to Coastline (m)	Weight
A	7.92	443.97	−38	15,295.6	0.118075	3.51
B	8.33	623.77	−149	15,295.6	0.083397	4
C	7.03	422.60	−39	9673.789	0.098445	3.51
D	10.74	1323.46	−26	6840.402	0.084664	3.96

. Underwater cables, protected areas, and main shipping routes are excluded to ensure that the candidate sites are free from these constraints. Furthermore, as stated in Table 3, Table 5 and Table 11, location A has a wind speed of 8.2 m/s, making it extremely suitable for wind energy generation. Its wind power density exceeds 500, its water depth is within a suitable range for installing fixed turbines, and the distance from the coastline is also in the acceptable range of 1.5 km to 100 km. The distance from power lines falls within class 3; however, this is a less important consideration than wind speed and wind power density.

The candidate areas shown in Table 11 and Table 12 are ordered according to the areas in km²; however, they can also be arranged by weight.

The Global Wind Atlas (GWA) [30], developed through a collaboration between the Technical University of Denmark (DTU) Wind Energy, and the World Bank, provides high-resolution wind data globally. The field measurement data for the Red Sea region are unavailable; therefore, to assess the reliability of wind energy assessment in our study area, we used field measurements from four coastal wind stations along the Red Sea coastline: Wejh, Yenbo, Jeddah-KAIA, and Jizan. This provided a reasonable basis for comparison to assess how well the wind atlas model aligns with observed conditions. For each of these stations, we examined data from the last 13 years of annual wind speed measurements. We then compared these data points to the corresponding outputs from the wind atlas model for the nearest site. Two statistical metrics were used to evaluate the accuracy of GWA data against field measurements:

• The Root Mean Square Error (RMSE) was used to quantify the magnitude of deviations;
• The Coefficient of Determination (R²) was used to assess the correlation between GWA estimates and field measurements.

The low RMSE value of 1.24 indicates that there was little variation between GWA predictions and field measurements, with the largest deviation of 1.8 (observed in the Jazan region) still falling within acceptable limits. The correlation analysis yielded an R² value of 0.75, as shown in Figure 13, demonstrating a strong positive relationship between GWA estimates and field measurements. While field measurements consistently showed slightly higher values than GWA predictions, this systematic difference was uniform across all locations. The consistency and predictability of this variation, combined with the strong correlation coefficient, support the reliability of GWA data for spatial suitability analysis in this region.

4.3. Energy Production

The number of turbines that can be installed at each location can be computed using the following equation [46]:

Number of turbines = total area + array spacing

(5)

The array spacing can be defined as the effective footprint for each turbine. If it is necessary to install the REpower 5M turbine with a 126 m rotor diameter, the array spacing is 0.794 km² [46]. Therefore, the number of turbines that can be installed at each location is shown in

Table 13. Comparing field measurements with the wind atlas model for four coastal stations.

Station	Latitude	Longitude	Annual Avg. Wind Field m/s	Annual Avg. GWA m/s
Wejh	26.199	36.476	7.9	6.5
Yenbo	24.144	38.063	7.5	6.9
Jeddah-KAIA	21.7	39.183	7.2	6.5
Jizan	16.901	42.586	6.3	4.5

and

Table 14. The potential energy generation for each location (floating turbines).

Location	Area (km2)	# of Turbines	Nameplate Capacity (MW)	Average Output (MWa)	Total Annual Generation (MWh/year)
A	3031.887	3818	19,092	6503	56,965,410
B	2166.873	2729	13,645	4648	40,712,870
C	2157.723	2718	13,588	4628	40,540,948
D	1425.097	1795	8974	3057	26,775,806
E	657.1447	828	4138	1409	12,346,938
F	597.0511	752	3760	1281	11,217,852

. To compute the total capacity without considering factors such as the wake effect and availability, the number of turbines should be multiplied by the capacity of a single turbine, which is 5 MW. However, to compute the average output, the capacity factor, which is 0.3406, is multiplied by the nameplate capacity. The total amount of energy generated annually can computed by multiplying the average output by the number of hours in a year (8760) [46].

Table 14 and

Table 15. The potential energy generation for each location (fixed turbines).

Location	Area (km2)	# of Turbines	Nameplate Capacity (MW)	Average Output (MWa)	Total Annual Generation (MWH/year)
A	141.489	178	891	303	2,658,404
B	122.2286	154	770	262	2,296,525
C	54.94649	69	346	118	1,032,377
D	46.63784	59	294	100	876,267

denote the area, number of turbines, nameplate capacity, average output, and total annual energy generation for locations deemed “extremely suitable”. For the floating turbines, which require a depth from 60 to 1000 m, each location has a suitable area ranging from 571 km² to 3032 km². The number of turbines that can be installed is directly proportional to the area size. The nameplate capacity, which represents the maximum amount of energy that can be generated in ideal conditions, ranges from 3760 MW at location F to 19,092 MW at location A. When determining the average output, the “wake effect” and “availability” factors are considered. The wake effect describes the decrease in energy production resulting from turbulence created by turbines, while availability refers to the amount of time for which a wind project is ready to function, considering maintenance, unexpected shutdowns, and weather conditions. We apply an integrated capacity factor of 0.3406 in our calculations, encompassing both the reduction in power output due to wake effects and the operational availability of wind turbines. This capacity factor of 0.3406 represents a realistic operational scenario based on industry standards and empirical data from existing offshore wind farms [46]. Therefore, the average outputs shown in Table 13 and Table 14 are significantly reduced compared with the nameplate capacity. The study acknowledges that actual wind farm output (average output) differs substantially from nameplate capacity due to various operational factors. To account for these real-world conditions, we incorporated an “all-in” capacity factor of 0.3406, which specifically addresses wake effects, maintenance-related downtime, and transmission losses.

The average output ranges from 1281 MWa in location F to 6503 MWa in location A. Moreover, the total annual energy production for location A exceeds 56,000,000 MWh/year. The findings showed that the Red Sea in Saudi Arabia offers a larger suitable area for floating turbines compared to fixed turbines, as shown in Table 14. Consequently, the energy that can be generated in areas with depths of 60–1000 m exceeds that of areas at depths of 5 to 60 m. In summation, the Red Sea can provide a significant contribution to the electricity demand.

5. Conclusions

In this study, we aimed to identify suitable locations for the installation of wind turbines in the offshore regions of Saudi Arabia. The criteria and constraints used to identify potential areas were wind speed, wind power density, proximity to power lines, distance from the coastline, water depth, underwater cables, protected areas, and the primary shipping route. We utilized the GIS-AHP model to evaluate appropriate locations for wind farms in the Red Sea and created a suitability map depicting appropriate locations for both floating and fixed turbines. The candidate areas were investigated and analyzed, and the potential energy generation for each location was computed. This paper provides several novel contributions. First, our study was the first to utilize the AHP approach to investigate suitable sites in offshore regions of Saudi Arabia. It identified locations that are “extremely suitable” for wind farms and describes their area and coordinates, and the total energy generation capacity was computed for each location.

A total area of 109,594 km² was determined to be unsuitable for floating turbines. However, a 11,077 km² area was identified as extremely suitable. In future work, it may be possible to explore the Arabian Gulf region to identify other suitable locations for offshore wind farms.