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Geospatial Analysis of Wind Energy Siting Suitability in the East African Community

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  • Samuel Bimenyimana

    (Intelligence and Automation in Construction Provincial Higher-Educational Engineering Research Centre, Huaqiao University, Xiamen 361021, China
    Hello Renewables Ltd., Kigali, Rwanda)

  • Chen Wang

    (Intelligence and Automation in Construction Provincial Higher-Educational Engineering Research Centre, Huaqiao University, Xiamen 361021, China
    Civil Engineering Department, Huaqiao University, Xiamen 361021, China)

  • Godwin Norense Osarumwense Asemota

    (African Centre of Excellence in Energy for Sustainable Development, University of Rwanda, Kigali, Rwanda
    Department of Electrical and Electronics Engineering, Morayo College, Thika 7053-01000, Kenya)

  • Jeanne Paula Ihirwe

    (Department of Economics and Management, Hebei University of Technology, Tianjin 300130, China)

  • Mucyo Ndera Tuyizere

    (Center for Geographical Information System and Remote Sensing, University of Rwanda, Kigali, Rwanda)

  • Fidele Mwizerwa

    (Center for Geographical Information System and Remote Sensing, University of Rwanda, Kigali, Rwanda
    Department of Spatial Planning, College of Sciences and Technology University, University of Rwanda, Kigali, Rwanda)

  • Yiyi Mo

    (Civil Engineering Department, Huaqiao University, Xiamen 361021, China)

  • Martine Abiyese

    (Hello Renewables Ltd., Kigali, Rwanda
    African Centre of Excellence in Energy for Sustainable Development, University of Rwanda, Kigali, Rwanda)

Abstract

Site investigation is essential for developing and constructing a dependable and effective wind engineering project. Also, the kinetic energy of moving air, used to drive a wind turbine, produces electricity. Having seen the shortage of previous studies on wind energy sites’ suitability across Africa and having read about the abundance of untapped wind energy resources in the East African region, this paper used Geographical Information System (GIS), multi-criteria, and Analytic Hierarchy techniques to provide a geospatial analysis of wind energy technology siting suitability in Eastern African Community Countries. Different data were acquired and processed from numerous open-access databases (Global Wind atlas, Regional Center for Mapping of Resources for Development (RCMRD), African Geoportal, East African community website, and Energy data Info.org). The results reveal Kenya has large parts of its land areas highly appropriate for wind energy siting (15.26%) and 1.55% of its land classified as unsuitable for wind energy generation. The rates of suitability and unsuitability were respectively 26.57% and 4.87% for Burundi, 20.6% and 10.21% for Rwanda, 20.39% and 10.44% for Tanzania, and 4.65% and 27.15% for South Sudan. The findings also show that East Africa exhibits moderate levels of wind energy siting suitability, with an estimated average of around 37.27% of its land area moderately suitable for wind energy technology installation, covering thousands of square kilometers. The study is advantageous to academia and industry-related personnel engaged in renewable energy-related activities in other African countries with similar topographies.

Suggested Citation

  • Samuel Bimenyimana & Chen Wang & Godwin Norense Osarumwense Asemota & Jeanne Paula Ihirwe & Mucyo Ndera Tuyizere & Fidele Mwizerwa & Yiyi Mo & Martine Abiyese, 2024. "Geospatial Analysis of Wind Energy Siting Suitability in the East African Community," Sustainability, MDPI, vol. 16(4), pages 1-32, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:4:p:1514-:d:1337148
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    References listed on IDEAS

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    2. Md Rabiul Islam & Md Rakibul Islam & Hosen M. Imran, 2022. "Assessing Wind Farm Site Suitability in Bangladesh: A GIS-AHP Approach," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    3. Evans, Annette & Strezov, Vladimir & Evans, Tim J., 2009. "Assessment of sustainability indicators for renewable energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 1082-1088, June.
    4. Lirong Yin & Lei Wang & Jianqiang Li & Siyu Lu & Jiawei Tian & Zhengtong Yin & Shan Liu & Wenfeng Zheng, 2023. "YOLOV4_CSPBi: Enhanced Land Target Detection Model," Land, MDPI, vol. 12(9), pages 1-17, September.
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