A Systematic Review of integrated GIS and MCDM methodology for Wind Farm Site Selection: Trends, Methods, and Criteria

Emhemed Algazel; Ibrahim Badi

doi:10.31181/sor41202761

Authors

Emhemed Algazel College of Civil Aviation, Misrata, Libya Author https://orcid.org/0009-0000-8989-436X
Ibrahim Badi Department of Mechanical Engineering, Libyan Academy-Misrata, Misrata, Libya Author https://orcid.org/0000-0002-1193-1578

DOI:

https://doi.org/10.31181/sor41202761

Keywords:

Multi-criteria decision-making, MCDM, Geographic information system, GIS, Wind energy, Renewable energy

Abstract

Wind energy has emerged as one of the most promising renewable energy sources and a sustainable alternative to conventional fossil fuels. However, selecting suitable locations for wind farms is a complex process that depends on several factors, including social, technological, economic, and environmental aspects. Over the past decade, academics have increasingly employed combinations of Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) techniques to address the challenges of wind farm location selection. This tendency is reflected in the significant number of scholarly papers on the subject. To give researchers a state-of-the-art perspective and a thorough understanding of current approaches, a comprehensive review of 147 studies published between 2015 and 2025 was carried out. A constructed taxonomy that encompasses GIS software, application fields, uncertainty treatment, MCDM techniques, spatial resolution in GIS, and site selection criteria was used to classify the examined studies. The most popular MCDM techniques for criterion weighting and ranking of alternatives are the Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. An examination of the criteria reveals that wind speed, slope, land use, distance from transmission lines, road proximity, environmental restrictions, and social acceptance are the most frequently considered factors during wind farm site selection. Identifying research gaps, improving modelling techniques, and facilitating well-informed decision-making in future wind energy development initiatives are all made possible by these classifications and insights.

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