Advanced Prediction and Control for Offshore Energy: Harvesting, Conversion, Storage and Environmental Sustainability

Submission Deadline: 01 August 2026 View: 192 Submit to Special Issue

Guest Editors

Dr. Yongxiang Lei

Email: leiyongxiang1205@gmail.com

Affiliation: School of Engineering, University of Warwick, Coventry, United Kingdom

Homepage: www.leiyongxiang.com

Research Interests: renewable energy, machine learning, control

Dr. Ziyang Wang

Email: z.wang47@aston.ac.uk

Affiliation: School of Computer Science and Digital Technologies, Aston University, Birmingham, United Kingdom

Homepage:

Research Interests: applied AI, computer vision, robotics

Summary

Offshore environments provide vast opportunities for clean energy harvesting through wind, waves, tides, currents and ocean thermal gradients, and are expected to play a crucial role in achieving global carbon-neutral and Sustainable Development Goals (SDGs). At the same time, offshore energy systems face harsh and highly dynamic conditions, including strong winds and waves, complex multi-physics interactions, uncertain loads, and challenging grid integration constraints. These factors pose significant hurdles for the reliable harvesting, conversion, storage, and delivery of offshore energy at scale while preserving marine ecosystems. Advanced prediction and control methods are therefore essential to fully unlock offshore energy potential, ensuring safe operation, high efficiency, and environmental sustainability across the entire life cycle.

Recent advances in sensing, big data analytics, high-fidelity simulation, and artificial intelligence have opened new avenues for forecasting offshore resources, predicting structural and environmental loads, and designing intelligent control and optimization strategies for offshore energy systems. Examples include data-driven wind, wave and current forecasting for real-time operation; model predictive and robust control for floating offshore wind turbines and wave energy converters; co-design of control and structural systems; digital twins for lifetime performance prediction; and integrated control of hybrid energy parks with storage and grid interaction. At the same time, the growing emphasis on environmental and ecological impacts calls for integrated frameworks that couple prediction and control with life-cycle assessment, risk analysis, and eco-friendly operation strategies. Cross-disciplinary research that bridges fluid mechanics, control theory, power systems, data science, and marine environmental science is urgently needed.

This Special Issue of Energy Engineering aims to assemble the latest research advances in advanced prediction and control for offshore energy, spanning harvesting, conversion, storage and environmentally sustainable deployment. It seeks to address fundamental scientific questions and engineering challenges, highlight novel models, algorithms and control architectures, and showcase demonstrative applications that improve reliability, efficiency, resilience, and environmental performance of offshore energy systems.

Topics of interest include, but are not limited to, the following:
· Resource characterization and short-/long-term forecasting of offshore wind, wave, tidal and current energy using numerical models, remote sensing and data-driven methods
· Advanced control strategies for fixed and floating offshore wind turbines, wave and tidal energy converters, and hybrid offshore energy systems
· Model predictive control, robust and adaptive control, and nonlinear control for load mitigation, power maximization and fatigue-life extension in harsh marine environments
· Integrated prediction–control frameworks for co-optimizing structural design, mooring systems, platform dynamics, and power conversion
· Digital twins, reduced-order modeling and multi-fidelity simulation for real-time monitoring, anomaly detection, and lifetime performance prediction of offshore assets
· Coordinated control and optimization of multi-turbine farms and hybrid offshore parks (wind–wave–tidal–solar) including wake and array effects
· Grid integration, power smoothing and frequency support using offshore energy with energy storage (batteries, hydrogen, flywheels, compressed air, etc.) and advanced power electronics
· Data-driven and AI-based approaches (machine learning, deep learning, reinforcement learning) for forecasting, control, fault diagnosis, and decision support in offshore energy systems
· Condition monitoring, health prognosis and predictive maintenance, including self-powered sensing, remote diagnostics, and autonomous inspection (AUVs/ROVs, drones)
· Coupled techno-economic and environmental assessment of prediction and control strategies, including reliability analysis, risk assessment and resilience to extreme events and climate change
· Life-cycle assessment (LCA), environmental impact and ecological co-design, including noise, habitat disturbance, and coexistence with fisheries, shipping and marine protected areas
· Regulatory, safety and operation frameworks that integrate advanced prediction and control with standards, certification, and sustainable deployment pathways for offshore energy

We cordially invite you to submit your latest research and review papers to this Special Issue, and look forward to contributions that advance the science and engineering of offshore energy systems toward a secure, low-carbon and environmentally sustainable future.

Keywords