Lisansüstü Eğitim Enstitüsü
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Yazar "Abdi, Ayan Pierre" ile Lisansüstü Eğitim Enstitüsü'a göz atma
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ÖgeAn advanced decision-support system for the selection of wind farm sites: The case of djibouti(Graduate School, 2025-07-17) Abdi, Ayan Pierre ; Damcı, Atilla ; 501202017 ; Structure EngineeringWind energy stands out as an exciting alternative energy source that can help meet the growing demand for electricity in African countries. The geographical proximity of Djibouti to the Red Sea, combined with its arid and semi-arid climate, results in the generation of consistent and robust winds, thereby enhancing its potential for wind energy. The increasing energy needs in Djibouti highlight the importance of exploring renewable energy sources, and wind energy is shaping up to be a promising option. Despite its significant potential, previous studies have not thoroughly explored Djibouti to identify suitable locations for wind farms. In order to ensure the long-term efficiency and sustainability of wind energy projects, it is essential to identify appropriate locations for the construction of wind farms. This thesis aims to present a decision-support system featuring two modules, designed to help researchers and professionals in renewable energy identify the most suitable locations for wind farms. The initial module encompasses the identification of relevant criteria, the collection of pertinent data, the creation of a suitability map for each criterion, the calculation of the respective weights associated with these criteria, and the generation of a comprehensive final suitability map that takes into account all criteria where areas deemed unsuitable are excluded. Upon conducting a comprehensive review of the existing literature, seven primary criteria have been identified to evaluate the appropriateness of prospective sites for wind farm construction: wind velocity, changes in wind direction, ground slope, distance to urban areas, distance to road network, distance to energy transmission networks, and land use. The weights of the criteria were calculated by utilizing the CRITIC method. This method differs from conventional approaches that rely on personal judgments. Wind velocity and distance to energy transmission networks were found to be the most important criteria according to the results. Nevertheless, it has been determined that ground slope and land use were found to be the least important criteria in comparison to other criteria evaluated in this study. A final suitability map indicating potential locations for wind farms in Djibouti was created by taking into account the aforementioned criteria and their corresponding implications weights. According to the final suitability map, three regions identified as the most suitable for wind farm construction in Djibouti are the northeastern area (Obock region), the southeastern region (Arta and Ali Sabieh regions), and the southwestern region (Dikhil region). The objective of the second module is to further investigate the suitability map obtained by the first module to determine an exact coordinate on the suitability map that can be used by professionals when constructing wind farms. It employs multi- criteria decision-making methods (i.e., CoCoSo, MAIRCA, TOPSIS, and VIKOR) to evaluate and rank potential locations for wind farm construction. This approach facilitates the prioritization of selecting the most appropriate site for wind farms. In xxii order to prioritize the alternative locations, the site selection module utilizes the weights calculated by the suitability map generation module. The site selection module utilizes these weights as inputs for the multi-criteria decision-making methods— CoCoSo, MAIRCA, and TOPSIS—to prioritize potential sites for wind farms. It also allows users to select either a single multi-criteria decision-making method or multiple methods at the same time. In the scenario where the user selects a single multi-criteria decision-making method (i.e., CoCoSo) to rank potential sites for wind farms, the results indicate that the ten most appropriate sites, which are assessed according to seven criteria, are located in Goubetto town in Ali Sabieh region. In cases where the user prefers to utilize multiple methods, the module additionally determines whether the top ten outcomes derived from the chosen methods are in precise alignment with one another or not. If they match exactly, the module will then present the most appropriate locations for wind farms without any further investigation. In the scenario where the user selects multiple MCDM methods to rank potential sites for wind farms, CoCoSo and MAIRCA methods are selected to demonstrate the ranking process. The findings show that the top ten ranked alternatives are identical for both methods, leading to an exact match. The identified top ten most appropriate sites form a cluster of sites that are located in the southeastern region of Djibouti, which is called Goubetto town in Ali Sabieh region. On the other hand, when there isn't an exact match, the module uses an alternative multi-criteria decision-making method (i.e., VIKOR) to provide the decision-maker with the most appropriate locations among those obtained from different multi-criteria decision-making methods. This comprehensive investigation ensures the identification of the most suitable locations for wind farms. In order to determine if a scenario without an exact match occurs when the user selects multiple multi-criteria decision-making methods to rank potential sites for wind farms, the TOPSIS method is included among the previously selected methods to demonstrate the ranking process. The findings show that the top ten ranked alternatives obtained by the previously selected methods are not identical to the results obtained by the TOPSIS method. The results of further investigation to identify the most suitable locations for wind farms indicate that the southeastern region of Djibouti, particularly located in the Ali Adde town in Ali Sabieh region, stands out as the most suitable location for wind farms. This research extends the existing body of knowledge in the renewable energy field by simultaneously integrating advanced multi-criteria decision-making methods and geographic information system tools to address the challenge of wind farm site selection on an unprecedented scale and with precise coordinates. The proposed decision-support system is designed to exhibit flexibility and applicability across various contexts, as it is grounded in readily accessible real-world datasets. This data- driven system serves as a practical instrument for renewable energy planning, thereby allowing the framework to be potentially applicable to other regions with similar data availability. Using the proposed advanced decision-support system, researchers and professionals would be empowered to make strategic and well-informed decisions when selecting the most suitable site for a wind farm. Furthermore, the suggested decision support system equips professionals in Djibouti with practical insights, making it easier to shift towards sustainable energy independence. Although the proposed advanced decision-support system fills several gaps in the renewable energy literature, there are a number of limitations to the proposed advanced decision-support system. First, although the proposed advanced decision-support system employs seven key criteria to determine the most appropriate locations for wind xxiii farms, the criteria for site selection may vary based on the economic, social, and geographical conditions present in the target area. Future research could strengthen the framework by adding further criteria, including environmental and social aspects, while also applying it in different fields to assess the model's flexibility and effectiveness applicability. Secondly, although the advanced decision-support system was demonstrated using multi-criteria decision-making methods, the incorporation of additional multi-criteria decision-making methods for conducting sensitivity analysis may significantly improve its overall effectiveness. As a third limitation, the framework was tested only in Djibouti. Future research could broaden the application of this approach to other countries, modifying the criteria to fit local conditions. As a fourth limitation, the proposed advanced decision-support system was employed solely for the purpose of identifying appropriate locations for on-land wind farm sites. It could be adjusted to assess the potential for offshore wind energy in Djibouti, thereby delving into another promising avenue for sustainable energy development and broadening the framework's applicability to different forms of renewable energy.