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This chapter discusses the integration of artificial intelligence (AI) with wave energy converter (WEC) technology to enhance the sustainability and efficiency of renewable energy sources. SolidWorks-based simulations were also conducted in this study to test four WEC models, including CETO 5, RM5, RM6, and EAP, to evaluate flow dynamics, stress distribution, and thermal performance under various marine conditions. It was observed that CETO 5 outperformed flap-based and chamber-type designs, achieving a greater productivity, which is attributed to its buoyant piston driver assembly and hydraulic power take-off (PTO) system. Artificial intelligence (AI) is projected to increase wave energy power capture by 25%, while also reducing dependence on expensive instruments for sea-state predictions. Studies showed that advanced techniques such as deep learning and quantum machine learning enhance system optimisation and environmental impact assessments, thereby supporting ecological sustainability and regulatory compliance.

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Wave Energy Converters and AI

  • 章节

  • 发表日期：

    2026年

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  • AlHussain A. AlHadrami,
  • Girma T. Chala,
  • Alhaitham M. Alkalbani

摘要

This chapter discusses the integration of artificial intelligence (AI) with wave energy converter (WEC) technology to enhance the sustainability and efficiency of renewable energy sources. SolidWorks-based simulations were also conducted in this study to test four WEC models, including CETO 5, RM5, RM6, and EAP, to evaluate flow dynamics, stress distribution, and thermal performance under various marine conditions. It was observed that CETO 5 outperformed flap-based and chamber-type designs, achieving a greater productivity, which is attributed to its buoyant piston driver assembly and hydraulic power take-off (PTO) system. Artificial intelligence (AI) is projected to increase wave energy power capture by 25%, while also reducing dependence on expensive instruments for sea-state predictions. Studies showed that advanced techniques such as deep learning and quantum machine learning enhance system optimisation and environmental impact assessments, thereby supporting ecological sustainability and regulatory compliance.