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Wave Power Density Hotspot Distribution and Correlation Pattern Exploration in the Gulf of Mexico

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  • Chengcheng Gu

    (Mechanical and Industrial Engineering Department, Texas A&M University-Kingsville, Kingsville, TX 78363, USA)

  • Hua Li

    (Mechanical and Industrial Engineering Department, Texas A&M University-Kingsville, Kingsville, TX 78363, USA)

Abstract

Wave energy has been studied and explored because of its enormous potential to supply electricity for human activities. However, the uncertainty of its spatial and temporal variations increases the difficulty of harvesting wave energy commercially. There are no large-scale wave converters in commercial operation yet. A thorough understanding of wave energy dynamic behaviors will definitely contribute to the acceleration of wave energy harvesting. In this paper, about 40 years of meteorological data from the Gulf of Mexico were obtained, visualized, and analyzed to reveal the wave power density hotspot distribution pattern, and its correlation with ocean surface water temperatures and salinities. The collected geospatial data were first visualized in MATLAB. The visualized data were analyzed using the deep learning method to identify the wave power density hotspots in the Gulf of Mexico. By adjusting the temporal and spatial resolutions of the different datasets, the correlations between the number of hotspots and their strength levels and the surface temperatures and salinities are revealed. The R value of the correlation between the wave power density hotspots and the salinity changes from −0.371 to −0.885 in a negative direction, and from 0.219 to 0.771 in a positive direction. For the sea surface temperatures, the R values range from −0.474 to 0.393. Certain areas within the Gulf of Mexico show relatively strong correlations, which may be useful for predicting the wave energy behavior and change patterns.

Suggested Citation

  • Chengcheng Gu & Hua Li, 2022. "Wave Power Density Hotspot Distribution and Correlation Pattern Exploration in the Gulf of Mexico," Sustainability, MDPI, vol. 14(3), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1158-:d:729163
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    1. Bozzi, Silvia & Archetti, Renata & Passoni, Giuseppe, 2014. "Wave electricity production in Italian offshore: A preliminary investigation," Renewable Energy, Elsevier, vol. 62(C), pages 407-416.
    2. Stoutenburg, Eric D. & Jenkins, Nicholas & Jacobson, Mark Z., 2010. "Power output variations of co-located offshore wind turbines and wave energy converters in California," Renewable Energy, Elsevier, vol. 35(12), pages 2781-2791.
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    1. Francisco Haces-Fernandez & Hua Li & David Ramirez, 2022. "Analysis of Wave Energy Behavior and Its Underlying Reasons in the Gulf of Mexico Based on Computer Animation and Energy Events Concept," Sustainability, MDPI, vol. 14(8), pages 1-23, April.

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