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A Mediterranean Sea Offshore Wind classification using MERRA-2 and machine learning models

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  • Majidi Nezhad, Meysam
  • Heydari, Azim
  • Neshat, Mehdi
  • Keynia, Farshid
  • Piras, Giuseppe
  • Garcia, Davide Astiaso

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.

Suggested Citation

  • Majidi Nezhad, Meysam & Heydari, Azim & Neshat, Mehdi & Keynia, Farshid & Piras, Giuseppe & Garcia, Davide Astiaso, 2022. "A Mediterranean Sea Offshore Wind classification using MERRA-2 and machine learning models," Renewable Energy, Elsevier, vol. 190(C), pages 156-166.
    • Handle: RePEc:eee:renene:v:190:y:2022:i:c:p:156-166
    DOI: 10.1016/j.renene.2022.03.110
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