The world is fast becoming a global village due to the increasing daily requirement of energy by all population across the world while the earth in its form cannot change. The need for energy and its related services to satisfy human social and economic development, welfare and health is increasing  .
Utilizing wind energy has a long history and Iranian was the first whom used wind mills with vertical axis to flour grains and taking water from wells 200 B.C. Southern winds and particularly Sistan winds have traditionally been considered  . The extent of human demand on energy resources is always one of the critical issues. Trying to achieve an inexhaustible energy source has been always man’s ancient dream. All fossil energy sources such as oil, gas, coal, and uranium, will end one day  . Human civilization, which depends on energy, will be disrupted with the exhaustion of non-renew- able fossil energies. On the other hand, the consumption of fossil energy production sources has its own costs of material and environmental problems. The use of nuclear energy regardless of the environmental consequences such as nuclear waste, is expensive and requires advanced technology. This made human beings always seek new energy sources to replace these two sources of energy; the wind energy is affordable and available, and also its use does not create pollution. With regard to geographical, economic, environmental and geological parameters, one can find potential regions by application of GIS for wind farms. So far, several studies performed on the possibility of using wind energy potential in the different geographical areas including: Sharif Moghaddasi  studied wind energy in Iran. The study came to the conclusion that potential of wind energy is very high in Iran, and if this energy is used in correct way then can significantly impact the economy (reducing the cost of electricity…), and also reduces environmental pollution from emissions such as carbon dioxide generated.
Hamouda  has evaluated the economic feasibility of wind power in Cairo. In this study, half-hourly wind speed data used throughout the year 2009, and calculations indicated that although the wind energy resources in Cairo is poor, but wind power can be used to supply industry energy.
Masseran, et al.  evaluated wind power density obtained from several density function of wind speed in Malaysia. And based on the average density map of electric power in Malaysia several areas such as the North East, North West, the south area of the coast of Malaysia and the south of Sabah region determined as areas that show the best locations for wind energy development.
Baban and Parry  examined development and application of an approach using GIS to locate wind farms in the UK. They use two different methods to combine GIS data layers of Lancashire stations. First, all layers were considered equally important and given equal weight. Second, layers of information grouped and were ranked according to the degree of importance. They showed that these maps can be used to help the decision-making process and to find a suitable location for wind farms.
In other research, Bennui et al.  selected a suitable place for large wind turbines using GIS. The research was performed on five provinces of Thailand with the aim of applying a comprehensive GIS system by combining multiple criteria decision making (MCDM) to choose the more efficient location of wind turbines in the country. In this study, we used parameters of wind speed, altitude, slope, highways, railways, built areas, forest areas and scenic areas; finally, the best places to install wind turbines on the east coast of Thailand from Nakhon Si Thammarat province to Tom Mart Narathiwas have been selected.
In this study, we attempted to identify the susceptible areas in the province using GIS and AHP method for the construction of wind farms and evaluated the area potential of electricity production.
2. Material and Methods
2.1. The Study Area
The study region, with an area of about 187,502 square kilometers, is one of the largest provinces of Iran, which is located between latitude of 25˚3" and 31˚27" north from and longitude of 58˚50" to 63˚21" east from the Greenwich meridian and this province has one of the lowest population density. This province consists of two regions of Sistan and Baluchestan and is bounded to South Khorasan and Afghanistan from north, to Pakistan and Afghanistan from east, to Oman Sea from South and to Kerman and Hormozgan provinces from west  . The location of study area is shown in Figure 1.
The location of wind farms as a question of making a decision needs materials and special tools. In this study, the data for period of 25 years from weather station in the province was used, and for spatial analysis and mapping criteria such as climatic, geographic, socio-economic, environmental and geological parameters ArcGIS 10.2.2 soft- ware was used. In order to study land use, we applied Landsat ETM + satellite related to 2014 and ENVI + 8 software was used for data analysis and interpretation. For weighting Information layers, Expert choice software was used. Figure 2 shows various stages of research.
2.2. AHP Processing Method
AHP is one of the best ways to decide when decision maker has multiple criteria  because they assist analysts or decide to organize the sensitive and vital issues  . Analytic hierarchy process begins identifying and prioritizing the elements of decision-making including objectives, criteria and potential options that may be used in prioritizing process. In the process, identifying elements and the relationship between
Figure 1. The location of study area in Iran.
Figure 2. Various research steps.
them is to create a hierarchical structure. Because structure of summarizing of decision-making elements is such as the chain of at different levels it is hierarchal. So, to create a hierarchical structure of the object under study is the first step in the analytic hierarchy process and objectives, criteria and options, as well as their relationship are shown in the same structure. The next steps in the analytic hierarchy process, criteria and sub-criteria weight measurement (coefficient of significance) and calculate the options coefficient of importance (weight), the final calculation of options and check logical consistency of judgments    .
In all processes, particularly the process of locating, evaluating is emphasized as one of the important parts of planning. Thus, after determining the overall aims and stated objectives and provide various options to achieve optimum location, the evaluation is conducted such that the desirable or better options to be selected based on adequacy  .
Criteria are commonly used to assess the relative merits of each option. Select a suitable site for the construction of wind farms, or in other words the positioning this rule is no exception. AHP model starts identifying the decision-making process and elements and prioritize them, these elements include different ways of doing things and prioritize the features  .
2.3. Hierarchical Structure Creation
AHP enables decision makers to provide a complex structure as a simple hierarchy. It also enables them to assess lots of qualitative and quantitative factors systematically in a multiple criteria situation  . Using the analytic hierarchy process to solve complex problems is usually performed in four stages  .
1) Degrading complex problems to a number of minor elements and then forming a hierarchy for the elements.
2) Pairs comparison of elements according to a ratio scale.
3) Using eigenvalues (matrix) to estimate the relative weight of the elements.
4) Using relative weights sum and combination of options for the final measurement. In other words, in any hierarchical structure relating to a specific subject, one faces with four hierarchical levels: objectives, criteria, sub-criteria, and options   .
2.4. Determining the Importance of Criteria and Sub-Criteria
Priority among the options is determined by a pair-wised comparison in AHP. The pair-wised comparison is done such that one of the options is selected and the priority of both is measured  . In this process the numbers 1 to 9 are applied as a standard scale to determine the importance of the options (from equal importance to extremely high importance). In a paired comparison matrix, 9 indicates extremely high importance of an option compared to other and 1.9 indicates that an option is extremely low important compared to other while 1 shows equal importance   . So, if the importance of first factor to the second factor gained the importance of the second factor to the first factor will be reciprocal (Table 1). Also ratio scale and verbal comparison are applied to weight quantifiable non-quantifiable elements  .
2.5. Determining Final Score (Priority) of Options
Coefficients of the criteria and the sub-criteria significance associated with the study as well as significance coefficients (scores) of options in relation to each of the sub-criteria are determined and specified already. At this point, we will determine the final score of each of the options integrating significance coefficients. To do so, we will use Saaty’s “principle of hierarchical combination” that leads to the vector of priority, with respect to all judgments at all levels of the hierarchy  . The following equation shows how to calculate the final score of an option. Final score of options:
where: is significance coefficient of criterion k, is significance coefficient of sub-criterion i, gij is the score of option j related to sub-criterion I  .
2.6. Compliance Rates
Table 1. Points scale of 9 hourly for paired comparison    .
priorities achieved by members of group or the priorities of combined tables. Experience has shown that if the consistency rate (CR) is less than 0.10, consistency of comparisons can be accepted, otherwise comparisons must be repeated  .
3. Results and Discussion
3.1. Weighting of Criteria
The first step is to determine the weighting of criteria. These weights are determined regarding the significance of measures against each other proportionate to the purpose of “locating the construction of wind farms”. The main criteria are initially compared to each layer. Table 2 compares the test criteria of the original layer in the location of wind farms and Figure 3 shows the calculated weight standards through the Expert Choice software.
3.2. Climate Criteria
Climate criteria are among the most important parameters for construction of wind power plants. In this research, climate elements was more important than the other criteria and thus to have more weight. In this regard, climate parameters, wind speed, dominant wind speed, pressure and temperature are among climate sub-criteria that have been selected for the location of wind farms.
3.3. Geographical Criteria
Geographical criteria are major factors of locating and construction of wind farms. The geographic sub-criteria under study are altitude and slope of the ground which after weighting in the ArcGIS were studied and analyzed.
3.4. Socio-Economic Criteria
Other important criteria must be considered for the location of wind farms are socio- economic ones. Socio-economic criteria include the criteria of distance communication
Table 2. Paired comparison of main criteria’s for locating wind farm plants.
Figure 3. The calculated weights of geological criteria.
(minor roads, major roads, and railways), distance from towns and villages (population centers).
3.5. Environmental Criteria
Now, noting environmental issues for locating wind farms is an important research purposes worldwide. Environmental criteria including the sub-criteria of distance from protected areas, land use and the distance from the river.
3.6. Geological Criteria
The following geological criteria used in this study: the distance from the epicenter of the earthquake (seismic) and distance to faults.
3.7. Sub-Criteria Weights
Given that for each criterion, sub-criteria have been defined, at this stage, for weighting the sub-criteria, they are mutually compared. Thus for each of them, paired comparison is done separately. In remaining sections, we will discuss them one by one.
3.8. Climatic Sub-Criteria
Wind speed, dominant wind speed, pressure and temperature are among the climate parameters which after weighting in Software Expert choice they are analyzed and evaluated in the software ArcGIS. Table 3 shows paired comparison of the climate criteria and Figure 4 shows the calculated weights.
3.9. Geographical Sub-Criteria
Geographical sub-criteria include altitude and slope. After being weighted in the Expert Choice software, these layers were analyzed in a GIS environment. Figure 5 shows calculated weights and Table 4 compares the geographical Sub-criteria in a paired form.
Table 3. Paired comparison of climate criteria.
Figure 4. The calculated weights of climate sub-criteria.
3.10. Socio-Economic Sub-Criteria
Socio-economic sub-criteria include distance from roads, distance from the cities and the villages. After being weighted by ArcGIS software, these criteria were analyzed such that after producing restores of distance from each one the weights assigned to all various layers of sub-criteria. Table 5 compares socio-economic in a paired form and Figure 6 shows a comparison of the socio-economic sub-criteria.
3.11. The Environmental Standards
Distance from the protected areas, land use and the distance from the river have been considered as the environmental sub-criteria and after weighing entered into ArcGIS software and they were analyzed using the Spatial Analyst extension. This analysis includes the restores production for the sub-criteria and assigning calculated weights. Table 6 shows paired comparison of sub-criteria for environmental criteria. Figure 7 shows a comparison of the environmental sub-criteria.
3.12. Weight of Options
After determining the weight of sub-criteria, we determined the weight of options and evaluated the significance of each option versus the other. Here, because of the multiplicity of comparisons, we only indicated 2 of them (wind speed and fault) (Table 7 and
Figure 5. The calculated weights of geographical sub-criteria.
Figure 6. Paired comparison of socio-economic sub-criteria.
Table 4. Paired comparison of geographical sub-criteria.
Table 5. Paired comparison of the socio-economic sub-criteria.
3.13. Determining the Final Score (Priority) of Options
At this point, the final score was determined for each of the options combining the mentioned significance coefficient. To do so, Saaty’s “principle of hierarchical combination” was used that leads to the priority vector with respect to all judgments at all
Table 6. Paired comparison of sub-criteria for environmental criteria.
Table 7. Paired comparison of wind speed options.
Table 8. Paired comparison of fault options.
Figure 7. Calculated weights of sub-criteria for environmental criteria.
Figure 8. Calculated weights of wind in various speeds.
levels of the hierarchy  . In simple terms by multiplying each of the criteria to the relevant sub-criteria and by multiplying obtained number to points corresponding score, the final score is obtained for each of the options.
3.14. Integrating Information Layers
After all information layers being provided and determining affective factors on locating wind farm plants and their roles over locating and through modeling and spatial analysis via GIS; we practiced to provide maps of those factors. After weighting the layers involved in locating wind farms based on the analytic hierarchy process (AHP), the capabilities of geographic information systems (GIS) were applied to integrate and overlap maps and locations map for wind plants was prepared. The resulting map was classified in four classes (excellent, good, fair and poor). An excellent area for the construction of wind farms in the north area of the study is located in Zabol station with an area of 72.0789842 hectares. Good areas with an area of over 95.186327 hectare in traces include southwest stations of Zabol, Zahedan, Saravan, Khash and Chabahar. However, the fair class with an area of over 79.9285437 hectare includes a wide range of South, Central and East area of the study covering approximately 0.53% of province area. Table 9 and Figure 10 show areas and locations suitable for construction of the wind farm, respectively.
3.15. Minimum Restrictions Assigned on Layers
Over operations and localization researches and in order to prevent waste of time and money, destruction of the environment and animal life as well as protect against natural disasters (earthquakes, floods…), minimum limits considered. This was performed on ArcGIS in a binary form or 0 and 1. Areas located within determined distance were assigned 0 and those in a suitable position assigned 1. Figure 11 shows a map of areas bearing limitation. Table 10 and Table 11 show characteristics indicating areas with
Figure 9. Calculated weights of distance from fault.
Table 9. Characteristics of location map.
Figure 10. Location of suitable areas for wind farm construction.
Figure 11. Location of limited areas.
Table 10. Limiting factors, the minimum and maximum distance from the studied criteria  .
restrictions and limiting factors, the minimum and maximum distance criteria, respectively.
3.16. Prioritizing Areas Suitable for Construction of Wind Farms
To determine the areas prioritized for constructing wind farms, two maps of wind farms locating and wind farms regional restrictions for the construction are consistent with each other. With that in mind such as areas with high potential and the low limits, their area, climatic factors and the field visit, the priorities were determined to build wind farms (Figure 12). Table 12 shows forecasted priorities for the construction of wind farms. According to this table, the areas around the Zabol station are considered first and second priority for constructing wind farms. However, the region experiences 120 days windy condition and the speed and power of the winds will increase the amount of power produced by wind turbines. The third priority to build a wind farm
Table 11. Characteristics of limited areas.
Table 12. Anticipated priorities for the construction of wind farms.
Figure 12. Prioritization of constructing wind farms in the study area.
was considered Chabahar. Because the area according to Map 12 has low and very low limits, and the wind speed is dominant and is a vast area compared to other stations with a good average. In this section, the essential point is that places like Zahak located in the eastern part of Zabol and has prevailing winds with average speed of 16 knots (the country’s Meteorological Agency, the average wind speed data in statistical terms, between 1985 to 2010) and in this case will increase the power of the wind turbines production rate, but in this study because the restrictive criteria (fault, river, village, town, land use…) have also been considered caused this area having many restrictions and too much economically limits not to be considered as a suitable site for wind farm construction and for this it couldn’t find a good rank among experts’ prioritization.
Wind turbines should be located at the sites with speedy wind and suitable continuity and lack of environmental barriers. In addition, the dominant wind speed and continuity are also very important factors. However, the vast, flat and almost circular dunes are the most suitable location to install wind turbines. Using AHP and based on specified criteria, the various parts of the region were prioritized regarding the establishment of wind farms. Based on the final map, areas for construction of wind farms in the province were identified. The results indicate a high potential for the construction of wind farms in cities of Zabol and Chabahar. The areas have been determined with regard to a series of factors including wind speed, dominant wind speed, scope, and limits. In this study, among criteria of climatic, geographic, environmental, economic, social and geological, climatic and geographical factors such as wind speed, dominant wind speed, slope and height of the most important location-finding construction of wind farms have been evaluated. In this study, an excellent area for the construction of wind farms in the north area of the study is located in Zabol station with an area of 72.0789842 hectares. Good areas with an area of over 95.186327 hectare in traces include southwest stations of Zabol, Zahedan, Saravan, Khash and Chabahar. However, the fair class with an area of over 79.9285437 hectare includes a wide range of South, Central and East area of the study covering approximately 0.53% of province area. Findings of this research indicate capability of the GIS in the modeling and helping to plan environmentally as well as combining qualitative and quantitative criteria through various scales. Locating and analyzing with AHP method help planners to take decisions based on better spatial data. It is certainly much more precise criteria to be used more and more to obtain favorable results.
 Owusu, P.A. and Asumadu-Sarkodie, S. (2016) A Review of Renewable Energy Sources, Sustainability Issues and Climate Change Mitigation. Civil and Environmental Engineering, 3, 1167990.
 Asumadu-Sarkodie, S. and Owusu, P.A. (2016) The Potential and Economic Viability of Wind Farms in Ghana. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 38, 695-701.
 Masseran, N., Razali, A.M. and Ibrahim, K. (2012) An Analysis of Wind Power Density Derived from Several Wind Speed Density Functions: The Regional Assessment on Wind Power in Malaysia. Renewable and Sustainable Energy Reviews, 16, 6476-6487.
 Bennui, A., Rattanamanee, P.P., Puetpaiboon, U., Phukpattaranont, P.P. and Chetpattananondh, K.K. (2007) Site Selection for Large Wind Turbine Using GIS. PSU-UNS International Conference on Engineering and Environment, Thailand.
 Bevilacqua, M., D’Amore, A. and Polonara, F. (2004) A Multi-Criteria Decision Approach to Choosing the Optimal Blanching-Freezing System. Journal of Food Engineering, 63, 253-263.
 Cheng, C.H. (1996) Evaluating Naval Tactical Missile Systems by Fuzzy AHP Based on the Grade Value of Membership Function. European Journal of Operational Research, 96, 343-350.
 Bertolini, M. and Braglia, M. (2006) Application of the AHP Methodology in Making a Proposal for a Public Work Contract. International Journal of Project Management, 24, 422-430.
 Chang, K.F., Chiang, C.M. and Chou, P.C. (2005) Adapting Aspects of GB Tool 200— Searching for Suitability in Taiwan. Building and Environment, 42, 310-316.
 Sarkis, J. and Talluri, S. (2004) Evaluating and Selecting E-Commerce Software and Communication Systems for a Supply Chain. European Journal of Operational Research, 159, 318-329.
 Poheker, S.D. and Ramachadran, M. (2004) Application of Multi-Criteria Decision Making to Sustainable Energy Planning—A Review. Renewable and Sustainable Energy Reviews, 8, 365-381.
 Kholil, R.O. (2013) Application of AHP Method for Selecting the Best Strategy to Reduce Environmental Damage Caused by Non-Metallic Mining Case Study in Gunungkidul Regency, Yogyakarta, Indonesia. International Journal of Environmental Engineering Science and Technology Research, 7, 98-109.
 Moreno-Jimenez, J.M. (2005) A Spreadsheet Module for Consistent Consensus Building in AHP-Group Decision Making. Group Decision and Negotiation, 14, 89-108.