More robust offshore wind energy planning through model ensembling

Journal Article

Title: More robust offshore wind energy planning through model ensembling

Author:

Depellegrin, D.; Ambrosino, M.; Roy, S.; Sanabria, J.; Llambrich, C.

Publication Date:

December 5, 2024

Journal:

npj Ocean Sustainability

Volume:

Issue:

Pages:

1-16

Publisher:

Springer Nature

Affiliation

University of Girona, IHCantabria

Technology:

Wind Energy, Fixed Offshore Wind

Receptor:

Human Dimensions, Marine Spatial Planning

Document Access

Website:

External Link

Attachment:

Access File

Citation

Depellegrin, D.; Ambrosino, M.; Roy, S.; Sanabria, J.; Llambrich, C. (2024). More robust offshore wind energy planning through model ensembling. npj Ocean Sustainability, 3(58), 1-16. https://doi.org/10.1038/s44183-024-00080-8

@article{Depellegrin-2024-2080358,
author = {Depellegrin, D and Ambrosino, M and Roy, S and Sanabria, J and Llambrich, C},
title = {More robust offshore wind energy planning through model ensembling},
journal = {npj Ocean Sustainability},
year = {2024},
month = {dec},
publisher = {Springer Nature},
volume = {3},
number = {58},
pages = {1--16},
doi = {10.1038/s44183-024-00080-8},
url = {https://www.nature.com/articles/s44183-024-00080-8},
keywords = {Wind Energy, Fixed Offshore Wind, Human Dimensions, Marine Spatial Planning},
}

Export Citation to BibTex

TY - JOUR
TI - More robust offshore wind energy planning through model ensembling
AU - Depellegrin, D
AU - Ambrosino, M
AU - Roy, S
AU - Sanabria, J
AU - Llambrich, C
T2 - npj Ocean Sustainability
AB - This research performs an ex-ante assessment of the 19 high potential areas for offshore wind energy (HPA-OWE) allocated in four maritime spatial planning subdivisions of Spain. A 39 geo-statistical criteria pool was developed and categorized into five planning tiers (coexistence, socio-ecological, spatial-efficiency, energy-equity, technical/technological). An ensemble of three multi-criteria decision analysis (MCDA) techniques coupled with a Monte Carlo method based on a large, uniform number of randomly distributed criteria weights is applied for more robust priority rankings of HPA-OWE. The co-existence tier indicates that HPA-OWE should be prioritized in the North Atlantic and in the Levantine–Balearic planning subdivision. The application of machine learning on the MCDA results identified criteria that most influence the rank of each HPA-OWE at planning subdivision. The outcomes highlight the need to include place-based data to better take into account spatial inequalities in coastal regions and re-balance them with socio-economic and energetically privileged coastal territories.
DA - 2024/12//
PY - 2024
PB - Springer Nature
VL - 3
IS - 58
SP - 1
EP - 16
UR - https://www.nature.com/articles/s44183-024-00080-8
DO - 10.1038/s44183-024-00080-8
LA - English
KW - Wind Energy
KW - Fixed Offshore Wind
KW - Human Dimensions
KW - Marine Spatial Planning
ER -

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Abstract

This research performs an ex-ante assessment of the 19 high potential areas for offshore wind energy (HPA-OWE) allocated in four maritime spatial planning subdivisions of Spain. A 39 geo-statistical criteria pool was developed and categorized into five planning tiers (coexistence, socio-ecological, spatial-efficiency, energy-equity, technical/technological). An ensemble of three multi-criteria decision analysis (MCDA) techniques coupled with a Monte Carlo method based on a large, uniform number of randomly distributed criteria weights is applied for more robust priority rankings of HPA-OWE. The co-existence tier indicates that HPA-OWE should be prioritized in the North Atlantic and in the Levantine–Balearic planning subdivision. The application of machine learning on the MCDA results identified criteria that most influence the rank of each HPA-OWE at planning subdivision. The outcomes highlight the need to include place-based data to better take into account spatial inequalities in coastal regions and re-balance them with socio-economic and energetically privileged coastal territories.