Geospatial Analysis of Scour in Offshore Wind Farms

Clara Matutano Molina (), Christian Velasco-Gallego, Nerea Portillo-Juan, Vicente Negro Valdecantos and Nieves Cubo-Mateo
Additional contact information
Clara Matutano Molina: Grupo de Investigación ARIES, Universidad Antonio de Nebrija, C. de Sta. Cruz de Marcenado, 27, 28015 Madrid, Spain
Christian Velasco-Gallego: Grupo de Investigación ARIES, Universidad Antonio de Nebrija, C. de Sta. Cruz de Marcenado, 27, 28015 Madrid, Spain
Nerea Portillo-Juan: Universidad Politécnica de Madrid, Campus Ciudad Universitaria, Calle del Profesor Aranguren 3, 28040 Madrid, Spain
Vicente Negro Valdecantos: Universidad Politécnica de Madrid, Campus Ciudad Universitaria, Calle del Profesor Aranguren 3, 28040 Madrid, Spain
Nieves Cubo-Mateo: Grupo de Investigación ARIES, Universidad Antonio de Nebrija, C. de Sta. Cruz de Marcenado, 27, 28015 Madrid, Spain

Energies, 2023, vol. 16, issue 15, 1-21

Abstract: Climate change has highlighted the need to promote renewable energies. The offshore wind industry is growing exponentially because of some political strategies supported by various organizations, such as the European Union. The implementation of these strategies is commonly associated with significant investments, public acceptance, or achieving better installations and greater cumulative capacities. To ensure that offshore renewable energy projects could reach their ambitious targets, this study promotes the implementation of political strategies or planning decisions using data mining techniques and analytical tools. Strategic decisions based on real data analysis could help to achieve more suitable and optimal infrastructures. The scour phenomenon jeopardizes the operability of offshore wind farms, making it necessary to study its evolution over the years. In this work, extensive research on the scour phenomenon in offshore wind farms using real data (from the Lynn and Inner Dowsing offshore wind farms located in the UK) was performed, which revealed an evident lack of consideration of this phenomenon for data-driven decision-making processes. As a novelty, this research develops a detailed geospatial analysis of data, studying the possible autocorrelation of scour data measured from each turbine between 2011 and 2015. The conclusions obtained could be used to improve future planning tasks in offshore wind farms.

Keywords: geospatial analysis; scour; offshore wind farms; climate change; spatial dependence; Moran index (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/15/5616/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/15/5616/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:15:p:5616-:d:1202707

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().