Artificial Intelligence-Driven Advanced Wave Energy Control Technology

Submission Deadline: 28 February 2026 View: 368 Submit to Special Issue

Guest Editors

Prof. Bo Yang

Email: yangbo_ac@outlook.com

Affiliation: Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, 650500, China

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Research Interests: optimization and control of new energy systems based on artificial intelligence

Dr. Shuai Zhou

Email: zoey.zhou@aut.ac.nz

Affiliation: Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland, 1010, New Zealand

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Research Interests: power system control, integration of renewable energy (wind energy and PV systems), smart grids, artificial intelligence applied to power systems

Dr. Yaxing Ren

Email: yren@lincoln.ac.uk

Affiliation: School of Engineering and Physical Sciences, University of Lincoln, Lincoln, LN6 7TS, United Kingdom

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Research Interests: modelling and control of renewable energy systems

Summary

The global pursuit of the "dual carbon" goal and the rapid development of renewable energy technologies have driven the large-scale development of ocean energy, especially wave energy, as a clean energy source with high energy density and strong predictability, is becoming an important component of the future energy system. At the same time, breakthroughs in artificial intelligence (AI) technology provide unprecedented opportunities for optimizing the control strategy of wave energy converter (WEC), improving energy capture efficiency, reducing operation and maintenance costs, and achieving coordinated operation of wave energy with offshore wind, photovoltaic, hydrogen production and other systems.

Intelligent wave energy control technology is the key to achieving efficient, stable, and sustainable development of marine energy, and is becoming a core driving factor for improving renewable energy utilization, enhancing grid compatibility, and reducing levelized energy costs. The WEC is a core component of the ocean energy system, and its operational efficiency directly affects the overall power generation performance. For the power grid, the intermittency and randomness of wave energy pose serious challenges to the stability, reliability, and economy of the power system. At the same time, the deployment and operation of wave energy devices are subject to multiple constraints such as marine environmental conditions, equipment durability, and grid connection technology. In addition, the mixed utilization of wave energy with other renewable energy sources poses higher requirements for collaborative control and optimized scheduling. In this context, in-depth research on the intelligent control of WECs, optimization of hydrogen production in electrolytic cells, management of wake effects, and multi energy collaborative planning using AI technology is of great significance for improving the economy, reliability, and grid adaptability of wave energy.

This section aims to explore and study the application of AI in the optimization control of WECs, collaborative management of offshore hydrogen production, optimization of wake effects, and planning of multi energy hybrid systems, and discuss the challenges, opportunities, and development trends in this field. We invite researchers and experts from around the world to submit high-quality original research papers and review articles on potential future topics, working together to promote the development of intelligent wave energy technology.

Potential topics aim at covering themes including, but not limited to:
1. Research on adaptive control strategy of WEC based on AI;
2. AI optimization of collaborative operation technology for offshore electrolysis hydrogen production system driven by wave energy;
3. Optimization management technology for wake effect of wave energy device array based on AI;
4. Application research of multi-agent reinforcement learning in collaborative control of wave energy clusters;
5. Capacity planning and optimization of wind wave solar hybrid energy systems driven by AI;
6. AI enhanced wave energy generation prediction and smart grid collaborative management technology;
7. Intelligent operation and maintenance system for wave energy device combining digital twin and AI.

Keywords

Published Papers