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

Deveci, Muhammet; Özcan, Ender; John, Robert; Pamucar, Dragan; Karaman, Himmet

Abstract

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.

Citation

Deveci, M., Özcan, E., John, R., Pamucar, D., & Karaman, H. (2021). Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS. Applied Soft Computing, 109, Article 107532. https://doi.org/10.1016/j.asoc.2021.107532

• Journal Article Type: Article
• Acceptance Date: May 17, 2021
• Online Publication Date: Jun 1, 2021
• Publication Date: 2021-09
• Deposit Date: Dec 2, 2021
• Publicly Available Date: Jun 2, 2022
• Journal: Applied Soft Computing
• Print ISSN: 1568-4946
• Electronic ISSN: 1872-9681
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 109
• Article Number: 107532
• DOI: https://doi.org/10.1016/j.asoc.2021.107532
• Keywords: Renewable energy; Wind power; MARCOS; WASPAS; MAIRCA; MABAC
• Public URL: https://nottingham-repository.worktribe.com/output/5633177
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S1568494621004555?via%3Dihub