A GIS-MCDA BASED ASSESSMENT FOR SITING WIND FARMS AND ESTIMATION OF THE TECHNICAL GENERATION POTENTIAL FOR WIND POWER IN SERBIA

References

• Ali, S., J. Taweekun, K. Techato, J. Waewsak, and S. Gyawali. 2019. GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand. Renewable Energy 132:1360–72. doi:10.1016/j.renene.2018.09.035.
   Web of Science ®Google Scholar
• Al-Yahyai, S., Y. Charabi, A. Gastli, and A. Al-Badi. 2012. Wind farm land suitability indexing using multi-criteria analysis. Renewable Energy 44:80–87. doi:10.1016/j.renene.2012.01.004.
   Web of Science ®Google Scholar
• Atici, K. B., A. B. Simsek, A. Ulucan, and M. U. Tosun. 2015. A GIS-based multiple criteria decision analysis approach for wind power plant site selection. Utilities Policy 37:86–96. doi:10.1016/j.jup.2015.06.001.
   Web of Science ®Google Scholar
• Aydin, N. Y., E. Kentel, and S. Duzgun. 2010. GIS-based environmental assessment of wind energy systems for spatial planning: A case study from Western Turkey. Renewable and Sustainable Energy Reviews 14 (1):364–73. doi:10.1016/j.rser.2009.07.023.
   Web of Science ®Google Scholar
• Ayodele, T. R., A. S. O. Ogunjuyigbe, O. Odigie, and J. L. Munda. 2018. A multi-criteria GIS based model for wind farm site selection using interval type-2 fuzzy analytic hierarchy process: The case study of Nigeria. Applied Energy 228:1853–69. doi:10.1016/j.apenergy.2018.07.051.
   Web of Science ®Google Scholar
• Baban, S. M., and T. Parry. 2001. Developing and applying a GIS-assisted approach to locating wind farms in the UK. Renewable Energy 24 (1):59–71. doi:10.1016/s0960-1481(00)00169-5.
   Web of Science ®Google Scholar
• Badang, D. A. Q., C. F. Sarip, and A. P. Tahud. 2018. Geographic information system (GIS) and multicriteria decision making (MCDM) for optimal selection of hydropower location in Rogongon, Iligan City. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Baguio City, Philippines, 1–5. doi:10.1109/HNICEM.2018.8666266.
   Google Scholar
• Bauer, L., and S. Matysik. 2020. GE General Electric GE 2.75 – 120. Accessed May 1, 2020. https://en.wind-turbine-models.com/turbines/983-ge-general-electric-ge-2.75-120#models
   Google Scholar
• Blanco,M. I. 2009. The economics of wind energy. Renewable and Sustainable Energy Reviews 13 (6–7):1372–82. doi:10.1016/j.rser.2008.09.004.
   Web of Science ®Google Scholar
• Bognar, A. 1990. Geomorfološke i inženjersko-geomorfološke osobine otoka Hvara i ekološko vrednovanje reljefa [Geomorphological and engineering-geomorphological characteristics of the island of Hvar and ecological evaluation of relief]. Hrvatski Geografski Glasnik 52 (1):49–64. https://hrcak.srce.hr/37480.
   Google Scholar
• Ćalić, J., M. Milošević, M. Milivojević, and T. Gaudenyi. 2017. Reljef Srbije [Relief of Serbia]. In Geografija Srbije [Geography of Serbia], ed. M. Radovanović, 22–93. Belgrade: Geographical Institute “Jovan Cvijić” SASA.
   Google Scholar
• Dai, K., A. Bergot, C. Liang, W. N. Xiang, Z. Huang, et al. 2015. Environmental issues associated with wind energy – A review. Renewable Energy 75:911–21. doi:10.1016/j.renene.2014.10.074.
   Web of Science ®Google Scholar
• Dalla Longa, F., T. Kober, J. Badger, P. Volker, C. Hoyer-Klick, I. Hidalgo Gonzalez, H. Medarac, W. Nijs, S. Politis, D. Tarvydas, et al. 2018. Wind potentials for EU and neighbouring countries: input datasets for the JRC-EU-TIMES model. EUR 29083 EN, publications office of the european union, Luxembourg, LUX. Accessed May 1, 2020. https://publications.jrc.ec.europa.eu/repository/bitstream/JRC109698/kjna29083enn_1.pdf.
   Google Scholar
• Defne, Z., K. A. Haas, and H. M. Fritz. 2011. GIS based multi-criteria assessment of tidal stream power potential: A case study for Georgia, USA. Renewable and Sustainable Energy Reviews 15 (5):2310–21. doi:10.1016/j.rser.2011.02.005.
   Web of Science ®Google Scholar
• Díaz-Cuevas, P. 2018. GIS-based methodology for evaluating the wind-energy potential of territories: A case study from Andalusia (Spain). Energies 11 (10):2789. doi:10.3390/en11102789.
   Web of Science ®Google Scholar
• Dragović, N. M., M. D. Vuković, and D. T. Riznić. 2019. Potentials and prospects for implementation of renewable energy sources in Serbia. Thermal Science 23 (5 Part B):2895–907. doi:10.2298/TSCI170312056D.
   Web of Science ®Google Scholar
• Ducić, V., and M. Radovanović. 2005. Klima Srbije [Climate of Serbia]. Belgrade, Serbia: Zavod za udžbenike.
   Google Scholar
• Đurišić, Ž., M. Bubnjević, D. Mikičić, and N. Rajaković. 2007. Wind Atlas of Vojvodina, Serbia. Paper presented at the European Wind Energy Conference & Exhibition (EWEC 2007), Milano, Italy, May 7–10.
   Google Scholar
• Đurišić, Ž., and J. Mikulović. 2012. Assessment of the wind energy resource in the South Banat region, Serbia. Renewable and Sustainable Energy Reviews 16 (5):3014–23. doi:10.1016/j.rser.2012.02.026.
   Web of Science ®Google Scholar
• Effat, H. A. 2014. Spatial modeling of optimum Zones for wind farms using remote sensing and geographic information system, application in the Red Sea, Egypt. Journal of Geographic Information System 6 (4):358–74. doi:10.4236/jgis.2014.64032.
   Google Scholar
• Enevoldsen, P., F. H. Permien, I. Bakhtaoui, A. K. von Krauland, M. Z. Jacobson, G. Xydis, B. K. Sovacool, S. V. Valentine, D. Luecht, G. Oxley, et al. 2019. How much wind power potential does europe have? Examining european wind power potential with an enhanced socio-technical atlas. Energy Policy 132:1092–100. doi:10.1016/j.enpol.2019.06.064.
   Web of Science ®Google Scholar
• European Commission. 2011. Communication from the commission to the european parliament, the council, the european economic and social committee and the committee of the regions: energy roadmap 2050. Accessed May 1, 2020. https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1588323650081&uri=CELEX:52011DC0885
   Google Scholar
• European Commission. 2016. EU reference scenario 2016: Energy, transport and GHG emissions Trends to 2050. Luxembourg: Publications Office of the European Union. doi:10.2833/001137.
   Google Scholar
• European Environment Agency. 2016. European digital elevation model (EU-DEM) version 1.1 [dataset]. Copernicus Land Monitoring Service. Accessed April 4, 2020. http://land.copernicus.eu/pan-european/satellite-derived-products/eu-dem/eu-dem-v1.1/view
   Google Scholar
• European Environment Agency. 2019a. Corine land cover (CLC) 2018, version 2020_20u1 [dataset]. Copernicus Land Monitoring Service. Accessed April 4, 2020. https://land.copernicus.eu/pan-european/corine-land-cover/clc2018
   Google Scholar
• European Environment Agency. 2019b. EU-hydro – river network database, version 1.1 [dataset]. Copernicus Land Monitoring Service. Accessed March 15, 2020. https://land.copernicus.eu/imagery-in-situ/eu-hydro/eu-hydro-river-network-database
   Google Scholar
• European Parliament, Council of the European Union. 2009. Directive 2009/28/EC of the european parliament and of the council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing directives 2001/77/EC and 2003/30/EC (text with EEA relevance). Accessed May 1, 2020. http://data.europa.eu/eli/dir/2009/28/oj
   Google Scholar
• European Parliament, Council of the European Union. 2018. Directive (EU) 2018/2001 of the european parliament and of the council of 11 December 2018 on the promotion of the use of energy from renewable sources (text with EEA relevance). Accessed May 1, 2020. http://data.europa.eu/eli/dir/2018/2001/oj
   Google Scholar
• European Wind Energy Association. 2009. Wind energy – The facts: A guide to the technology, economics and future of wind power. London: Earthscan.
   Google Scholar
• Eurostat. 2020a. Share of renewable energy in gross final energy consumption by sector [dataset]. Accessed May 1, 2020. https://ec.europa.eu/eurostat/product?code=NRG_IND_REN&mode=view
   Google Scholar
• Eurostat. 2020b. Wind and water provide most renewable electricity [dataset]. Accessed May 1, 2020. https://ec.europa.eu/eurostat/web/products-eurostat-news/-/DDN–20200129–1
   Google Scholar
• Evans, A., V. Strezov, and T. J. Evans. 2009. Assessment of sustainability indicators for renewable energy technologies. Renewable and Sustainable Energy Reviews 13 (5):1082–88. doi:10.1016/j.rser.2008.03.008.
   Web of Science ®Google Scholar
• Gburčik, P., V. Gburčik, M. Gavrilov, V. Srdanović, and S. Mastilović. 2006. Complementary regimes of solar and wind energy in Serbia. Geographica Pannonica 10 (10):22–25. doi:10.5937/GeoPan0610022G.
   Google Scholar
• Gburčik, V., S. Mastilović, and Ž. Vučinić. 2013. Assessment of solar and wind energy resources in Serbia. Journal of Renewable and Sustainable Energy 5 (4):041822. doi:10.1063/1.4819504.
   Web of Science ®Google Scholar
• Geosrbija. 2019. Protected cultural property [dataset]. digital platform - open data NSDI service. Accessed March 29, 2020. https://geosrbija.rs/en/services-eng/open-data-of-nsdi-eng/
   Google Scholar
• Ghobadi, M., and M. Ahmadipari. 2018. Environmental planning for wind power plant site selection using a fuzzy PROMETHEE-based outranking method in geographical information system. Environmental Energy and Economic Research 2 (2):75–87. doi:10.22097/EEER.2018.148760.1041.
   Google Scholar
• Gigović, L., D. Pamučar, D. Božanić, and S. Ljubojević. 2017. Application of the GIS-DANP-MABAC multi-criteria model for selecting the location of wind farms: A case study of Vojvodina, Serbia. Renewable Energy 103:501–21. doi:10.1016/j.renene.2016.11.057.
   Web of Science ®Google Scholar
• Harker, P. T., and L. G. Vargas. 1987. The theory of ratio scale estimation: Saaty’s analytic hierarchy process. Management Science 33 (11):1383–403. doi:10.1287/mnsc.33.11.1383.
   Web of Science ®Google Scholar
• Höfer, T., Y. Sunak, H. Siddique, and R. Madlener. 2016. Wind farm siting using a spatial analytic hierarchy process approach: A case study of the Städteregion Aachen. Applied Energy 163:222–43. doi:10.1016/j.apenergy.2015.10.138.
   Web of Science ®Google Scholar
• Institute for Multidisciplinary Research. 2004. Studija energetskog potencijala srbije za korišćenje sunčevog zračenja i energije vetra [study of energy potential of Serbia for utilizing solar radiation and wind energy]. NPEE, Reg. No. EE704-1052A. Accessed ‎May 1, 2020. http://vetar-sunce.imsi.rs/tekstovi/Studija_EE704-1052A/
   Google Scholar
• Institute for Nature Conservation of Serbia. n.d. Map of protected areas of Serbia [dataset]. https://cloud.gdi.net/visios/zzps
   Google Scholar
• IRENA. 2019. Global energy transformation: A roadmap to 2050. 2019 ed. international renewable energy agency, Abu Dhabi. Accessed May 1, 2020. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Apr/IRENA_Global_Energy_Transformation_2019.pdf
   Google Scholar
• Jahangiri, M., R. Ghaderi, A. Haghani, and O. Nematollahi. 2016. Finding the best locations for establishment of solar-wind power stations in Middle-East using GIS: A review. Renewable and Sustainable Energy Reviews 66:38–52. doi:10.1016/j.rser.2016.07.069.
   Web of Science ®Google Scholar
• Jangid, J., A. K. Bera, M. Joseph, V. Singh, T. P. Singh, B. K. Pradhan, and S. Das. 2016. Potential zones identification for harvesting wind energy resources in desert region of India–A multi criteria evaluation approach using remote sensing and GIS. Renewable and Sustainable Energy Reviews 65:1–10. doi:10.1016/j.rser.2016.06.078.
   Web of Science ®Google Scholar
• Janke, J. R. 2010. Multicriteria GIS modeling of wind and solar farms in Colorado. Renewable Energy 35 (10):2228–34. doi:10.1016/j.renene.2010.03.014.
   Web of Science ®Google Scholar
• Joint Research Centre. 2016. Flood hazard map for Europe - 100-year return period [dataset]. European Union Open Data Portal. Accessed April 4, 2020. http://data.europa.eu/88u/dataset/jrc-floods-floodmapeu_rp100y-tif
   Google Scholar
• Kokotović Kanazir, V., J. Stojilković Gnjatović, M. Filipović, S. Babović, M. Ivković, and S. Lović Obradović. 2017. Stanovništvo Srbije [Population of Serbia]. In Geografija Srbije [Geography of Serbia], ed. M. Radovanović, 506–613. Belgrade, Serbia: Geographical Institute “Jovan Cvijić” SASA.
   Google Scholar
• Komarov, D., S. Stupar, A. Simonović, and M. Stanojević. 2012. Prospects of wind energy sector development in Serbia with relevant regulatory framework overview. Renewable and Sustainable Energy Reviews 16 (5):2618–30. doi:10.1016/j.rser.2012.01.067.
   Web of Science ®Google Scholar
• Latinopoulos, D., and K. Kechagia. 2015. A GIS-based multi-criteria evaluation for wind farm site selection. A regional scale application in Greece. Renewable Energy 78:550–60. doi:10.1016/j.renene.2015.01.041.
   Web of Science ®Google Scholar
• Law on Spatial plan of the Republic of Serbia from 2010 to 2020. 2010. Official Gazette of the Republic of Serbia No 88/2010. Belgrade, Serbia: Official Gazette.
   Google Scholar
• Mardani, A., A. Jusoh, E. K. Zavadskas, F. Cavallaro, and Z. Khalifah. 2015. Sustainable and renewable energy: an overview of the application of multiple criteria decision making techniques and approaches. Sustainability 7 (10):13947–84. doi:10.3390/su71013947.
   Web of Science ®Google Scholar
• Masters, G. M. 2004. Renewable and efficient electric power systems. Hoboken, New Jersey: John Wiley & Sons.
   Google Scholar
• McKenna, R., S. Hollnaicher, and W. Fichtner. 2014. Cost-potential curves for onshore wind energy: A high-resolution analysis for Germany. Applied Energy 115:103–15. doi:10.1016/j.apenergy.2013.10.030.
   Web of Science ®Google Scholar
• McKenna, R., S. Hollnaicher, P. O. Vd Leye, and W. Fichtner. 2015. Cost-potentials for large onshore wind turbines in Europe. Energy 83::217–29. doi:10.1016/j.energy.2015.02.016.
   Web of Science ®Google Scholar
• Mikičić, D., B. Radičević, and Ž. Ðurišić. 2006. Wind energy potential in the world and in Serbia and Montenegro. Facta Universitatis, Series: Electronics and Energetics 19:47–61. http://facta.junis.ni.ac.rs/eae/fu2k61/radicevic.pdf.
   Google Scholar
• Milovanović, B., M. Radovanović, G. Stanojević, M. Pecelj, and J. Nikolić. 2017. Klima Srbije [Climate of Serbia]. In Geografija Srbije [Geography of Serbia], ed. M. Radovanović, 94–159. Belgrade: Geographical Institute “Jovan Cvijić” SASA.
   Google Scholar
• Ministry of Energy, Development and Environmental Protection of the Republic of Serbia. 2013. National renewable energy action plan of the republic of Serbia. Accessed May 1, 2020. https://www.mre.gov.rs/doc/efikasnost-izvori/NREAP%20OF%20REPUBLIC%20OF%20SERBIA%2028_June_2013.pdf?uri=CELEX:32009L0028
   Google Scholar
• Ministry of Mining and Energy. n.d. Decree on establishment of implementation program of the energy sector development strategy of the republic of Serbia for the period to 2025 year with projections to 2030, the year of the period 2017 to 2023 year. Accessed May 1, 2020. https://www.mre.gov.rs/doc/efikasnost-izvori/PROGRAM%20FOR%20THE%20IMPLEMENTATION%20ENERGY%20STRATEGY%20for%20the%20period%20from%202017%20until%202023.pdf
   Google Scholar
• Ministry of Mining and Energy of the Republic of Serbia. 2016. Energy sector development strategy of the Republic of Serbia for the period by 2025 with projections by 2030. Accessed May 1, 2020. https://www.mre.gov.rs/doc/efikasnost-izvori/23.06.02016%20ENERGY%20SECTOR%20DEVELOPMENT%20STRATEGY%20OF%20THE%20REPUBLIC%20OF%20SERBIA.pdf
   Google Scholar
• Ministry of Mining and Energy of the Republic of Serbia. 2020. Register of privileged producers of electricity, temporary privileged producers of electricity and producers from renewable energy sources 11.03.2020. Accessed March 29, 2020. https://www.mre.gov.rs/doc/registar-110320.html#Sec_Vetar
   Google Scholar
• Monforti, F., T. Huld, K. Bódis, L. Vitali, M. D’isidoro, and R. Lacal-Arántegui. 2014. Assessing complementarity of wind and solar resources for energy production in Italy. A monte carlo approach. Renewable Energy 63:576–86. doi:10.1016/j.renene.2013.10.028.
   Web of Science ®Google Scholar
• Nasehi, S., S. Karimi, and H. Jafari. 2016. Application of fuzzy GIS and ANP for wind power plant site selection in East Azerbaijan Province of Iran. Computational Research Progress in Applied Science & Engineering 2 (3):116–24. http://www.crpase.com/archive/CRPASE-2016-VOL%2002-ISSUE%2003-05.pdf..
   Google Scholar
• Nematollahi, O., P. Alamdari, M. Jahangiri, A. Sedaghat, and A. A. Alemrajabi. 2019. A techno-economical assessment of solar/wind resources and hydrogen production: A case study with GIS maps. Energy 175:914–30. doi:10.1016/j.energy.2019.03.125.
   Web of Science ®Google Scholar
• Nikolić, N. 2017. Vetrenjače – Svuda gde je dozvoljeno i ekonomično [Windmills – Wherever allowed and economical]. In Srbija bez fosilnih goriva: Alternativni INDC: Alternativni scenario razvoja energetskog sektora do 2050. godine – Tranzicija na 100% obnovljive izvore energije [Serbia without fossil fuels: Alternative INDC: An alternative scenario energy sector development by 2050 – The transition to 100% renewable energy], ed. Đ. Samardžija, 19–21. Belgrade, Serbia: Jedan stepen Srbija.
   Google Scholar
• Noorollahi, Y., H. Yousefi, and M. Mohammadi. 2016. Multi-criteria decision support system for wind farm site selection using GIS. Sustainable Energy Technologies and Assessments 13:38–50. doi:10.1016/j.seta.2015.11.007.
   Web of Science ®Google Scholar
• OpenStreetMap. 2018. [dataset]. Accessed April 4, 2020. https://www.openstreetmap.org
   Google Scholar
• Pamučar, D., L. Gigović, Z. Bajić, and M. Janošević. 2017. Location selection for wind farms using GIS multi-criteria hybrid model: an approach based on fuzzy and rough numbers. Sustainability 9 (8):1315. doi:10.3390/su9081315.
   Web of Science ®Google Scholar
• Panwar, N. L., S. C. Kaushik, and S. Kothari. 2011. Role of renewable energy sources in environmental protection: A review. Renewable and Sustainable Energy Reviews 15 (3):1513–24. doi:10.1016/j.rser.2010.11.037.
   Web of Science ®Google Scholar
• Perpiña, C., J. C. Martínez-Llario, and Á. Pérez-Navarro. 2013. Multicriteria assessment in GIS environments for siting biomass plants. Land Use Policy 31:326–35. doi:10.1016/j.landusepol.2012.07.014.
   Web of Science ®Google Scholar
• QGIS (Version 3.12.1). 2020. https://qgis.org/en/site/forusers/download.html.
   Google Scholar
• REN21. 2019. Renewables 2019 global status report. REN21 Secretariat, Paris, France. https://www.ren21.net/wp-content/uploads/2019/05/gsr_2019_full_report_en.pdf.
   Google Scholar
• Ritchie, H., and M. Roser. 2020. Renewable energy. Published online at OurWorldInData.org. Accessed May 1, 2020. https://ourworldindata.org/renewable-energy
   Google Scholar
• Rodman, L. C., and R. K. Meentemeyer. 2006. A geographic analysis of wind turbine placement in Northern California. Energy Policy 34 (15):2137–49. doi:10.1016/j.enpol.2005.03.004.
   Web of Science ®Google Scholar
• Romanić, D., M. Ćurić, I. Jovičić, and M. Lompar. 2015. Long-term trends of the ‘Koshava’ wind during the period 1949–2010. International Journal of Climatology 35 (2):288–302. doi:10.1002/joc.3981.
   Web of Science ®Google Scholar
• Romanić, D., M. Ćurić, M. Lompar, and I. Jovičić. 2016. Contributing factors to Koshava wind characteristics. International Journal of Climatology 36 (2):956–73. doi:10.1002/joc.4397.
   Web of Science ®Google Scholar
• Romanić, D., M. Ćurić, M. Zarić, M. Lompar, and I. Jovičić. 2016. Investigation of an extreme Koshava wind episode of 30 January–4 February 2014. Atmospheric Science Letters 17 (2):199–206. doi:10.1002/asl.643.
   Web of Science ®Google Scholar
• Ryberg, D. S., M. Robinius, and D. Stolten. 2017. Methodological framework for determining the land eligibility of renewable energy sources. arXiv:1712.07840. https://arxiv.org/abs/1712.07840.
   Google Scholar
• Ryberg, D. S., M. Robinius, and D. Stolten. 2018. Evaluating land eligibility constraints of renewable energy sources in Europe. Energies 11 (5):1246. doi:10.3390/en11051246.
   Web of Science ®Google Scholar
• Ryberg, D. S., Z. Tulemat, D. Stolten, and M. Robinius. 2020. Uniformly constrained land eligibility for onshore European wind power. Renewable Energy 146:921–31. doi:10.1016/j.renene.2019.06.127.
   Web of Science ®Google Scholar
• Saaty, T. L. 1980. The Analytic Hierarchy Process. New York, NY: McGraw Hill.
   Google Scholar
• Saidur, R., N. A. Rahim, M. R. Islam, and K. H. Solangi. 2011. Environmental impact of wind energy. Renewable and Sustainable Energy Reviews 15 (5):2423–30. doi:10.1016/j.rser.2011.02.024.
   Web of Science ®Google Scholar
• Sánchez-Lozano, J. M., M. S. García-Cascales, and M. T. Lamata. 2014. Identification and selection of potential sites for onshore wind farms development in Region of Murcia, Spain. Energy 73:311–24. doi:10.1016/j.energy.2014.06.024.
   Web of Science ®Google Scholar
• Sánchez-Lozano, J. M., M. S. García-Cascales, and M. T. Lamata. 2016. GIS-based onshore wind farm site selection using fuzzy multi-criteria decision making methods. Evaluating the case of Southeastern Spain. Applied Energy 17 1:86–102. doi:10.1016/j.apenergy.2016.03.030.
   Google Scholar
• Sander + Partner. 2016. Wind atlas balkan [dataset]. Accessed May 1, 2020. https://balkan.wind-index.com/
   Google Scholar
• Shi, J., and E. Erdem. 2017. Estimation of Wind Energy Potential and Prediction of Wind Power. In Wind Energy Engineering: A Handbook for Onshore and Offshore Wind Turbines, ed.. T. M. Letcher, 25–49. London, United Kingdom: Academic Press. doi:10.1016/B978-0-12-809451-8.00003-5
   Google Scholar
• Siyal, S. H., U. Mörtberg, D. Mentis, M. Welsch, I. Babelon, and M. Howells. 2015. Wind energy assessment considering geographic and environmental restrictions in Sweden: A GIS-based approach. Energy 83:447–61. doi:10.1016/j.energy.2015.02.044.
   Web of Science ®Google Scholar
• Sliz-Szkliniarz, B., and J. Vogt. 2011. GIS-based approach for the evaluation of wind energy potential: A case study for the kujawsko–pomorskie voivodeship. Renewable and Sustainable Energy Reviews 15 (3):1696–707. doi:10.1016/j.rser.2010.11.045.
   Web of Science ®Google Scholar
• Srbije, E. 2002. Mogućnost korišćenja energije vetra za proizvodnju električne energije [Possibilities for wind energy utilization for electricity production]. Belgrade, Serbia: Elektroprivreda Srbije.
   Google Scholar
• Statistical Office of the Republic of Serbia. 2014. Comparative overview of the number of population in 1948, 1953, 1961, 1971, 1981, 1991, 2002 and 2011: data by settlements. statistical office of the republic of Serbia, Belgrade, Serbia. Accessed May 1, 2020. http://publikacije.stat.gov.rs/G2014/Pdf/G20144008.pdf
   Google Scholar
• Statistical Office of the Republic of Serbia. 2019. Statistical yearbook оf the republic of Serbia 2019. Statistical Office of the Republic of Serbia, Belgrade, Serbia. Accessed May 1, 2020. https://publikacije.stat.gov.rs/G2019/Pdf/G20192052.pdf
   Google Scholar
• Tegou, L. I., H. Polatidis, and D. A. Haralambopoulos. 2010. Environmental management framework for wind farm siting: Methodology and case study. Journal of Environmental Management 91 (11):2134–47. doi:10.1016/j.jenvman.2010.05.010.
   PubMed Web of Science ®Google Scholar
• Todorović, D., U. Marković, and Ž. Đurišić. 2013. Predikcija proizvodnje perspektivnih vetroelektrana u regionu banata [Prediction of production of perspective wind farms in Banat Region]. In Proceedings of the XII International-professional Symposium Infoteh-Jahorina 2013, ed. S. Milojković, 213–18. Istočno Sarajevo, Bosnia and Herzegovina: School of Electrical Engineering. https://infoteh.etf.ues.rs.ba/zbornik/2013/radovi/ENS-3/ENS-3-2.pdf
   Google Scholar
• Unkašević, M., J. Mališic, and I. Tošić. 1999. Some aspects of the wind ‘Koshava’in the lower troposphere over Belgrade. Meteorological Applications 6 (1):69–79. doi:10.1017/S1350482799000997.
   Web of Science ®Google Scholar
• Unkašević, M., J. Mališić, and I. Tošić. 1998. On some new statistical characteristics of the wind “Koshava”. Meteorology and Atmospheric Physics 66 (1–2):11–21. doi:10.1007/BF01030445.
   Web of Science ®Google Scholar
• Unkašević, M., I. Tošić, and M. Obradović. 2007. Spectral analysis of the “Koshava” wind. Theoretical and Applied Climatology 89 (3–4):239–44. doi:10.1007/s00704-006-0261-5.
   Web of Science ®Google Scholar
• Van Haaren, R., and V. Fthenakis. 2011. GIS-based wind farm site selection using spatial multi-criteria analysis (SMCA): Evaluating the case for New York State. Renewable and Sustainable Energy Reviews 15 (7):3332–40. doi:10.1016/j.rser.2011.04.010.
   Web of Science ®Google Scholar
• Villacreses, G., G. Gaona, J. Martínez-Gómez, and D. J. Jijón. 2017. Wind farms suitability location using geographical information system (GIS), based on multi-criteria decision making (MCDM) methods: The case of continental Ecuador. Renewable Energy 109:275–86. doi:10.1016/j.renene.2017.03.041.
   Web of Science ®Google Scholar
• Vukmirović, D. 1985. The spatial structure of the koshava wind. In 11th International Conference for Alpine Meteorology, 14–16 September 1983, 10–15. Budapest, Hungarian: Hungarian Meteorological Service.
   Google Scholar
• Walsh, C. 2020. Wind energy in Europe in 2019: trends and statistics. Brussels, Belgium: WindEurope. https://windeurope.org/wp-content/uploads/files/about-wind/statistics/WindEurope-Annual-Statistics-2019.pdf
   Google Scholar
• Watson, J. J., and M. D. Hudson. 2015. Regional scale wind farm and solar farm suitability assessment using GIS-assisted multi-criteria evaluation. Landscape and Urban Planning 138:20–31. doi:10.1016/j.landurbplan.2015.02.001.
   Web of Science ®Google Scholar
• Wikimapia. 2012. [dataset]. Accessed April 4, 2020. https://wikimapia.org
   Google Scholar
• Wizelius, T. 2012. Design and Implementation of a Wind Power Project. In Comprehensive Renewable Energy: Vol. 2. Wind Energy, ed. A. Sayigh, 391–430. Amsterdam, Netherlands: Elsevier.
   Google Scholar
• Wu, X., C. Zhang, L. Jiang, and H. Liao. 2020. An integrated method with PROMETHEE and conflict analysis for qualitative and quantitative decision-making: case study of site selection for wind power plants. Cognitive Computation 12 (1):100–14. doi:10.1007/s12559-019-09675-7.
   Web of Science ®Google Scholar
• Yalcin, M., and F. K. Gul. 2017. A GIS-based multi criteria decision analysis approach for exploring geothermal resources: Akarcay basin (Afyonkarahisar). Geothermics 67:18–28. doi:10.1016/j.geothermics.2017.01.002.
   Web of Science ®Google Scholar
• Zadeh, L. A. 1965. Fuzzy sets. Information and Control 8 (3):338–53. doi:10.1016/S0019-9958(65)90241-X.
   Google Scholar
• Zafirakis, D. P., A. G. Paliatsos, and J. K. Kaldellis. 2012. Energy yield of contemporary wind turbines. In Comprehensive Renewable Energy: Vol. 2. Wind Energy, ed. A. Sayigh, 113–68. Amsterdam, Netherlands: Elsevier.
   Google Scholar