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As a result, six alternative locations representing spatial concentration were identified. These alternatives were then evaluated using the fuzzy TOPSIS method, a multi-criteria decision-making method (MCDM), taking into account the ten criteria defined for this study. Expert assessments were expressed and transformed into triangular fuzzy numbers to capture uncertainty and subjectivity in the decision-making process. The results show six alternative options, ranked from the one with the highest proximity coefficient to the one with the lowest. The findings demonstrate that the integrated use of machine learning (ML) and fuzzy TOPSIS methods provides an effective and robust decision support framework for ESS location selection problems. This approach also serves as a guide for other renewable energy planning practices.<\/jats:p>","DOI":"10.3390\/systems14020200","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:42:13Z","timestamp":1770918133000},"page":"200","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Multi-Criteria Decision-Making Approach Integrated with Machine Learning for Energy Resource Supply"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5897-8680","authenticated-orcid":false,"given":"Erhan","family":"Baran","sequence":"first","affiliation":[{"name":"Department of Electronic and Automation, TUSAS-Kazan Vocational School, Gazi University, Ankara 06980, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bandeiras, F., Gomes, A., Gomes, M., and Coelho, P. 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