Scholarlink

The integration of Multi-Criteria Decision Making (MCDM) and Geographic Information Systems (GIS) in the clean energy field is an innovative and powerful approach that helps make informed and efficient decisions in the planning and development of renewable energy sources (RES) projects. Both tools combine their strengths to address the complexity of choosing and prioritizing locations, technologies, and strategies based on multiple criteria, such as sustainability, costs, environmental impact, and technical feasibility. The methodology for selecting optimal sites by merging MCDM and GIS consists of 3 phases. In Phase 1, constraint and selection factors are established, data is collected and the potential area is delimited. Phase 2 includes the design of a prototype plant, identification of alternatives and the construction of the decision matrix. In Phase 3, weights are assigned to the factors and a ranking of alternatives is obtained.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integration of MCDM with GIS and Case Studies

  • 章节

  • 发表日期：

    2025年

查看全文
  • Isabel C. Gil-García,
  • Adela Ramos-Escudero,
  • Luis Serrano-Gómez,
  • Ana Fernández-Guillamón

摘要

The integration of Multi-Criteria Decision Making (MCDM) and Geographic Information Systems (GIS) in the clean energy field is an innovative and powerful approach that helps make informed and efficient decisions in the planning and development of renewable energy sources (RES) projects. Both tools combine their strengths to address the complexity of choosing and prioritizing locations, technologies, and strategies based on multiple criteria, such as sustainability, costs, environmental impact, and technical feasibility. The methodology for selecting optimal sites by merging MCDM and GIS consists of 3 phases. In Phase 1, constraint and selection factors are established, data is collected and the potential area is delimited. Phase 2 includes the design of a prototype plant, identification of alternatives and the construction of the decision matrix. In Phase 3, weights are assigned to the factors and a ranking of alternatives is obtained.