Type-2 neutrosophic number based multi-attributive border approximation area comparison (MABAC) approach for offshore wind farm site selection in USA.

Deveci, Muhammet; Erdogan, Nuh; Cali, Umit; Stekli, Joseph; Zhong, Shuya

Abstract

The technical, logistical, and ecological challenges associated with offshore wind development necessitate an extensive site selection analysis. Technical parameters such as wind resource, logistical concerns such as distance to shore, and ecological considerations such as fisheries all must be evaluated and weighted, in many cases with incomplete or uncertain data. Making such a critical decision with severe potential economic and ecologic consequences requires a strong decision-making approach to ultimately guide the site selection process. This paper proposes a type-2 neutrosophic number (T2NN) fuzzy based multi-criteria decision-making (MCDM) model for offshore wind farm (OWF) site selection. This approach combines the advantages of neutrosophic numbers sets, which can utilize uncertain and incomplete information, with a multi-attributive border approximation area comparison that provides formulation flexibility and easy calculation. Further, this study develops and integrates a techno-economic model for OWFs in the decision-making. A case study is performed to evaluate and rank five proposed OWF sites off the coast of New Jersey. To validate the proposed model, a comparison against three alternative T2NN fuzzy based models is performed. It is demonstrated that the implemented model yields the same ranking order as the alternative approaches. Sensitivity analysis reveals that changing criteria weightings does not affect the ranking order.

Citation

DEVECI, M., ERDOGAN, N., CALI, U., STEKLI, J. and ZHONG, S. 2021. Type-2 neutrosophic number based multi-attributive border approximation area comparison (MABAC) approach for offshore wind farm site selection in USA. Engineering applications of artificial intelligence [online], 103, article 104311. Available from: https://doi.org/10.1016/j.engappai.2021.104311

• Journal Article Type: Article
• Acceptance Date: May 19, 2021
• Online Publication Date: Jun 3, 2021
• Publication Date: Aug 31, 2021
• Deposit Date: Jun 4, 2021
• Publicly Available Date: Jun 4, 2021
• Journal: Engineering applications of artificial intelligence
• Print ISSN: 0952-1976
• Electronic ISSN: 1873-6769
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 103
• Article Number: 104311
• DOI: https://doi.org/10.1016/j.engappai.2021.104311
• Keywords: Decision-making; Fuzzy sets; Type-2 neutrosophic number; Site selection; Offshore wind
• Public URL: https://rgu-repository.worktribe.com/output/1352716