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Wave energy assessment under climate change through artificial intelligence.

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This study optimized an artificial neural network (ANN) to assess wave energy resources for wave farms. The ANN accurately predicts long-term wave energy, considering climate change impacts like sea level rise.

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Area of Science:

  • Renewable Energy
  • Climate Change Adaptation
  • Oceanography

Background:

  • Renewable energy implementation is crucial for addressing climate change.
  • Accurate assessment of wave energy resources is vital for wave farm development.
  • Climate change, particularly sea level rise, impacts ocean conditions.

Purpose of the Study:

  • To optimize and utilize an artificial neural network (ANN) for assessing wave energy resources.
  • To evaluate the long-term wave energy potential for wave farms over a 25-year period.
  • To incorporate climate change scenarios, including sea level rise, into the assessment.

Main Methods:

  • Trained and validated artificial neural networks (ANNs) using extensive deep-water wave data.
  • Propagated deep-water sea states to various locations using the Delft3D-Wave numerical model.
  • Assessed cumulative wave energy at numerous locations under different sea level rise scenarios.

Main Results:

  • Cumulative wave energy increases with water depth.
  • Optimal wave energy resources are found at greater depths near coastal features and ports.
  • Projected sea level rise is expected to increase the available wave energy resource.

Conclusions:

  • The developed ANN effectively quantifies long-term wave energy, reducing computational costs.
  • The model aids in selecting optimal locations for wave farms, considering climate change effects.
  • Findings highlight the potential for increased wave energy resources due to rising sea levels.