Navigating the Waves of Change: AI in Unlocking Ocean Energy Potentials

In the ever-evolving landscape of renewable energy, the ocean stands out as a vast and underexploited source of power. Among the various types of offshore renewable energy, wind energy is notably one of the most viable and profitable. Highlighting its potential, the UK government has set a new objective to achieve an offshore wind capacity of 50 GW by 2030 [1]. Meanwhile, the European Union has established a target for offshore energy capacity, aiming for between 109 GW and 112 GW by the same year [2]. Alongside tidal, floating solar, and wave energy, offshore wind energy plays a crucial role in propelling the energy transition forward. However, the journey to harness this abundant power faces numerous obstacles, ranging from the harsh marine environments and the logistical complexities of offshore operations to the unpredictability of oceanic conditions.

Nowadays, given the burgeoning volume of available data and measures that are constantly being generated, the utilisation of the information that these provide has become increasingly pivotal in addressing renewable energy challenges [3]. The advent of Artificial Intelligence (AI) introduces revolutionary solutions that have the potential to transform the ocean energy sector [4]. By leveraging enhanced environmental understanding and operational efficiency, AI technologies offer innovative approaches. These include the advancement in metocean (meteorological and oceanographic) resource assessment, intelligent operation and maintenance planning, design optimisation, and efficient control. 

Let’s delve deeper into how Artificial Intelligence is steering the future of this sector, not only addressing current challenges, but also to unlocking new opportunities for renewable energy.

The Pivotal Role of AI in Ocean Energy Development

Artificial Intelligence in ocean energy extends beyond a mere technological trend; it has become a cornerstone in demystifying the complexities of the maritime domain. AI and machine learning algorithms are adept at processing and analysing extensive datasets, which include parameters such as wave patterns, tidal cycles, wind speeds, and ocean currents. Their ability to do so with unprecedented speed and accuracy significantly, surpasses traditional analytical methods. This sophisticated data analysis not only identifies the most favourable locations for deploying wind turbines and other renewable energy technologies, but also optimises energy capture and reduces the risk to equipment [5].

Recent applications of AI for renewable energy, particularly in optimizing wind farm layouts, incorporate metaheuristic algorithms and reinforcement learning [6]. These approaches are particularly effective in navigating the high-dimensional space of optimisation problems, while simultaneously addressing multiple techno-economic criteria. Furthermore, AI plays a vital role in the operation and maintenance of ocean energy systems (which, in terms of costs, impact between 20% and 25% of the LCOE compared to around 12% of onshore counterparts) [7]. AI facilitates comprehensive health monitoring [8], estimating the remaining useful life of components [9], and enabling predictive maintenance strategies [10]. This approach is more efficient than traditional corrective or scheduled maintenance methods.AI also aids in predicting and forecasting favourable weather windows for maintenance operations, thereby optimising logistical planning and execution.

Enhancing Ocean Energy Efficiency with AI

AI’s contribution to ocean energy extends beyond optimal farms development and into the realms of real-time monitoring and adaptive management of operations. By continuously analysing sensor data from ocean energy devices, AI algorithms are capable of detecting anomalies [11], predicting potential system failures [12], and suggesting preemptive actions. This capability not only minimises downtime but also extends the lifespan of equipment, thereby improving the overall efficiency of the energy plant. 

Moreover, the efficiency of energy conversion for individual wind turbines can be enhanced through AI-based optimal control strategies [13]. Techniques such as fuzzy logic controllers, data-driven neural networks, and deep reinforcement learning have shown promise in this area, being able to reduce the fatigue of the turbines, increase the operating time and life, and improve the power performances, without significant additional costs.
In summary, AI is instrumental in unlocking the full potential of ocean energy. Through its capacity to analyse vast datasets, predict operational challenges, and optimise both the design and maintenance of energy systems, AI is paving the way for a more efficient and sustainable future in renewable energy [2].

MESPAC, a commitment to innovation

The integration of Artificial Intelligence into Ocean Energy marks a significant leap towards achieving more sustainable and efficient renewable energy systems. As AI technology continues to evolve, it promises to unlock new possibilities for the Blue Economy sector. From optimising device design to facilitating grid integration and efficient energy storage solutions, AI is at the forefront of this transformative journey. Our exploration with AI is just beginning, and we are excited about the potential it holds to enhance the competitiveness of ocean energy projects.

At MESPAC, we are committed to leveraging artificial intelligence to navigate and unlock the ocean’s energy potential. Our goal is to deliver groundbreaking solutions that facilitate the efficient utilisation of sustainable renewable energy. Utilizing AI, we aim to mitigate uncertainties within the ocean energy sector by providing precise and reliable information regarding environmental conditions. As we sail through the currents of change in the renewable energy landscape, Artificial Intelligence serves as our compass, guiding us towards a future where Ocean Energy Efficiency is a tangible reality.

Join us on this journey as we unlock the boundless potential of the ocean for energy generation.

References

[1] Jones, F., 2023. UK offshore wind capacity needs to increase by 265% to meet 2030 targets, Power Technology.
https://www.power-technology.com/newsletters/uk-offshore-wind-capacity-must-grow-265-to-meet-2030-targets/

[2] Directorate-General for Energy, 2023. Member States Agree New Ambition for Expanding Offshore Renewable Energy, European Commission.
https://energy.ec.europa.eu/news/member-states-agree-new-ambition-expanding-offshore-renewable-energy-2023-01-19_en

[3] Kalogirou, S.A., 2001. Artificial neural networks in renewable energy systems applications: a review. Renewable and Sustainable Energy Reviews 5, 373–401.
https://www.sciencedirect.com/science/article/pii/S1364032101000065

[4] Masoumi, M., 2023. Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts. Journal of Marine Science Engineering 11, 1855.
https://www.mdpi.com/2077-1312/11/10/1855

[5] Zhang, K., et al., 2022. Study of the Performance of Deep Learning Methods Used to Predict Tidal Current Movement. Journal of Marine Science Engineering 11, 26.
https://www.mdpi.com/2077-1312/11/1/26

[6] Wu, Y.K., et al., 2014. Optimization of the Wind Turbine Layout and Transmission System Planning for a Large-Scale Offshore WindFarm by AI Technology. IEEE Transactions on Industry Applications, 50, 3, 2071-2080. https://ieeexplore.ieee.org/document/6607158

[7] Black, I.M., et al., 2021. Condition monitoring systems: a systematic literature review on machine-learning methods improving offshore-wind turbine operational management. International Journal of Sustainable Energy 40, 10, 923-946. https://www.tandfonline.com/doi/full/10.1080/14786451.2021.1890736

[8] De N Santos, F., et al., 2023. Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks. Renewable Energy, 205, 461-474. https://www.sciencedirect.com/science/article/pii/S0960148123001027

[9] Carroll J., et al. 2019. Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques. Wind Energy, 22, 360–375.
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[10] Colone, L., et al., 2019. Predictive repair scheduling of wind turbine drive-train components based on machine learning. Wind Energy, 22, 9, 1230-1242.
https://onlinelibrary.wiley.com/doi/epdf/10.1002/we.2352

[11] Xie, T., et al., 2023. The fault frequency priors fusion deep learning framework with application to fault diagnosis of offshore wind turbines. Renewable Energy, 202, 143-153. https://www.sciencedirect.com/science/article/pii/S0960148122017049

[12] Hsu, Y.J., et al., 2020. Wind Turbine Fault Diagnosis and Predictive Maintenance Through Statistical Process Control and Machine Learning. IEEE Access, Data Mining and Internet of Things, 8, 23427-23439. https://ieeexplore.ieee.org/abstract/document/8966331

[13] Zhang, Y., et al., 2022. Data-driven predictive control for floating offshore wind turbines based on deep learning and multi-objective optimization. Ocean Engineering 266, 2, 112820. https://www.sciencedirect.com/science/article/pii/S0029801822021035#!