This paper uses a Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) re-analysis to identify long-term Mediterranean Sea Offshore Wind (OW) classification possible locations. In particular, an OW classification based on the last 40-years period OW speeds highlighted the best areas for potential Offshore Wind Turbine Generators (OWTG) installations in the Mediterranean basin. Preliminary, long-term OW classification results show that several Mediterranean basin zones in the Aegean Sea, Gulf of Lyon, the Northern Morocco and Tunisia regions have attractive OW potential. Secondly, a combined forecasting model based on the wavelet decomposition method and long-term memory neural network has been developed to predict the short-term wind speed considering the last ten years of hourly data for Mediterranean areas. The results of the proposed model for wind speed prediction have been compared with other single models, Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), highlighting a higher level of accuracy. Finally, three Weibull fitting algorithms have been provided to analyze the wind energy potential in the Mediterranean basin.

A Mediterranean sea offshore wind classification using MERRA-2 and machine learning models / Majidi Nezhad, M.; Heydari, A.; Neshat, M.; Keynia, F.; Piras, G.; Astiaso Garcia, D.. - In: RENEWABLE ENERGY. - ISSN 0960-1481. - 190:(2022), pp. 156-166. [10.1016/j.renene.2022.03.110]

A Mediterranean sea offshore wind classification using MERRA-2 and machine learning models

Majidi Nezhad M.;Heydari A.;Piras G.;Astiaso Garcia D.
2022

Abstract

This paper uses a Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) re-analysis to identify long-term Mediterranean Sea Offshore Wind (OW) classification possible locations. In particular, an OW classification based on the last 40-years period OW speeds highlighted the best areas for potential Offshore Wind Turbine Generators (OWTG) installations in the Mediterranean basin. Preliminary, long-term OW classification results show that several Mediterranean basin zones in the Aegean Sea, Gulf of Lyon, the Northern Morocco and Tunisia regions have attractive OW potential. Secondly, a combined forecasting model based on the wavelet decomposition method and long-term memory neural network has been developed to predict the short-term wind speed considering the last ten years of hourly data for Mediterranean areas. The results of the proposed model for wind speed prediction have been compared with other single models, Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), highlighting a higher level of accuracy. Finally, three Weibull fitting algorithms have been provided to analyze the wind energy potential in the Mediterranean basin.
2022
long short-term memory; MERRA-2; multilayer perceptron; offshore wind classification; offshore wind farms installation; wavelet transform
01 Pubblicazione su rivista::01a Articolo in rivista
A Mediterranean sea offshore wind classification using MERRA-2 and machine learning models / Majidi Nezhad, M.; Heydari, A.; Neshat, M.; Keynia, F.; Piras, G.; Astiaso Garcia, D.. - In: RENEWABLE ENERGY. - ISSN 0960-1481. - 190:(2022), pp. 156-166. [10.1016/j.renene.2022.03.110]
File allegati a questo prodotto
File Dimensione Formato  
Majidi Nezhad_A Mediterranean Sea_2022.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 3.1 MB
Formato Adobe PDF
3.1 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1629852
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 24
  • ???jsp.display-item.citation.isi??? 21
social impact