Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS

Over the past 20 years, the development of offshore wind farms has become increasingly important across the world. One of the most crucial reasons for that is offshore wind turbines have higher average speeds than those onshore, producing more electricity. In this study, a new hybrid approach integr...

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Bibliographic Details
Published inApplied soft computing Vol. 109; p. 107532
Main Authors Deveci, Muhammet, Özcan, Ender, John, Robert, Pamucar, Dragan, Karaman, Himmet
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.09.2021
Subjects
MABAC
MAIRCA
MARCOS
Renewable energy
WASPAS
Wind power
MAIRCA
WASPAS
MABAC
Renewable energy
Wind power
MARCOS
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Summary:Over the past 20 years, the development of offshore wind farms has become increasingly important across the world. One of the most crucial reasons for that is offshore wind turbines have higher average speeds than those onshore, producing more electricity. In this study, a new hybrid approach integrating Interval Rough Numbers (IRNs) into Best-Worst Method (BWM) and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) is introduced for multi-criteria intelligent decision support to choose the best offshore wind farm site in a Turkey’s coastal area. Four alternatives in the Aegean Sea are considered based on a range of criteria. The results show the viability of the proposed approach which yields Bozcaada as the appropriate site, when compared to and validated using the other multi-criteria decision-making techniques from the literature, including IRN based MABAC, WASPAS, and MAIRCA. •A novel integrated MCDM approach to offshore wind farm site selection is presented.•6 main and 23 sub-criteria are considered for decision making.•Interval rough numbers is applied to obtain the weights for all criteria.•A real-world case study is carried out considering four coastal sites in Turkey.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2021.107532