Description:FIELD OF INVENTION
Advanced offshore wind energy systems, AI-driven optimization, renewable energy technologies, sustainable power generation, smart grid integration, and energy efficiency.
BACKGROUND OF INVENTION
Offshore wind energy has emerged as a vital resource for meeting global renewable energy demands due to stronger and more consistent wind conditions at sea. Traditional offshore wind systems rely on fixed or floating turbines with pre-programmed operational parameters, which often fail to adapt efficiently to dynamic marine environments. Existing methodologies mainly use static control strategies, such as pitch and yaw control, to optimize turbine performance. However, these approaches lack real-time adaptability, leading to suboptimal energy capture and increased maintenance costs. Recent advances incorporate Supervisory Control and Data Acquisition (SCADA) systems and basic predictive maintenance algorithms, yet they are limited in decision-making capabilities. To address these challenges, AI-optimized wind energy harvesting systems are being developed. These systems integrate artificial intelligence techniques like machine learning, adaptive control algorithms, and predictive analytics to dynamically adjust turbine operations in real time, enhancing efficiency, minimizing downtime, and maximizing offshore power generation.
The patent application number 202011014385 discloses a energy harvesting from far field rf signal. Energy harvesting from far-field rf signals involves capturing and converting distant radio frequency waves into usable electrical energy.
The patent application number 201941008399 discloses a system for harvesting wind energy. A system that captures wind energy using turbines, converting it into electrical power for sustainable and renewable energy generation.
The patent application number 202241013993 discloses a vertical axis wind turbine for wind energy based power generators. A vertical axis wind turbine captures wind from any direction, efficiently generating power for wind energy-based power generation systems.

SUMMARY
The invention proposes an AI-optimized wind energy harvesting system specifically designed to maximize offshore power generation. The system integrates advanced artificial intelligence algorithms to dynamically control and adjust turbine operations in real-time based on changing oceanic and atmospheric conditions. By analyzing wind patterns, sea states, and weather forecasts, the AI system optimizes turbine orientation, blade pitch, and energy storage management to ensure peak efficiency and minimal downtime. It also enhances predictive maintenance by detecting anomalies early, reducing operational costs, and extending turbine lifespan. The objective of the invention is to significantly increase the reliability, efficiency, and profitability of offshore wind farms while minimizing environmental impact. By leveraging AI-driven decision-making and predictive analytics, the system addresses the challenges of variable wind conditions and harsh marine environments, providing a smarter, more sustainable solution for large-scale renewable energy production.

DETAILED DESCRIPTION OF INVENTION
Introduction
The rise of machine learning (ML) and artificial intelligence (AI) has revolutionized various scientific and engineering fields, spanning from the early stages of research and development to the implementation of established methodologies and the communication of results. The offshore renewable energy sector is no exception to this transformation. As global efforts intensify to increase renewable energy production, challenges related to expanding current systems and constructing more sustainable and efficient infrastructures have become more urgent. This "race" to meet these demands has underscored the growing importance of ML in this domain. Specifically, ML techniques offer a suite of tools that can streamline design, optimization, development, and operational processes, making them more cost-effective and efficient.
This review aims to provide an in-depth exploration of the various ML techniques applied within the offshore wind energy sector, focusing on specific use cases. Rather than simply summarizing the findings of relevant studies, this work offers detailed insights into the methodologies employed and the outcomes achieved. The goal is to equip researchers with a comprehensive understanding of how ML has been integrated into offshore wind energy systems, enabling them to evaluate the effectiveness and potential of these approaches.
The literature is organized into three primary categories. The first category includes studies that use ML to predict ocean data characteristics, such as wind speed, direction, and wave patterns, as well as research on the environmental impacts of offshore wind farms. This section also covers work focused on identifying optimal locations for wind farms. The second category explores studies that employ ML to model the performance of wind farms, optimize their operations, and enhance control systems. The third category addresses the use of ML in structural health monitoring, damage identification, and the optimization of operation and maintenance procedures.
It is important to note that some studies may fit into more than one category. For instance, research that uses ML to predict environmental data, such as wind or wave patterns, is placed in the first category, while studies focusing on performance optimization and control systems are categorized in the second. The literature is reviewed chronologically within each category, with a "Prospective" section highlighting future directions and overarching perspectives on ML implementations in offshore wind energy.

Methods
To gather relevant literature for this review, both manual and systematic search methods were employed. The primary tool for systematic literature search was the USGS BiblioSearch [1], a cross-platform tool developed in Python that integrates multiple APIs to query databases such as Clarivate Web of Science and Elsevier Scopus. Additionally, the pybliometrics Python library [2] was used to obtain abstracts from the Scopus database.
The search query included terms like "machine learning," "neural networks," "deep learning," "reinforcement learning," "decision trees," "support vector machines," and various other data-driven modeling techniques, in combination with keywords such as "offshore wind energy," "offshore renewable energy," "floating wind," and "wind-wave farms." The results were filtered to ensure relevance, and additional relevant studies were added through manual searches.
Climatic Data Prediction and Environmental Effects
In offshore wind energy applications, accurate prediction of climatic data, such as wind speed and wave patterns, is crucial for optimizing energy production and ensuring efficient turbine operation. ML techniques provide decision-makers with valuable insights that enhance energy yield and economic feasibility. Furthermore, ML tools are instrumental in understanding the environmental impacts of offshore wind farms. These models can predict species distribution, map habitats, and assess collision risks, contributing to the development of environmentally sustainable wind farm installations.
ML also facilitates innovations like autonomous navigation through wind farm areas, noise reduction in sensor data, and the creation of comprehensive datasets for infrastructure identification. By leveraging techniques such as deep learning, spatial modeling, and wave analysis, ML is reshaping the understanding of marine environments and enhancing the efficiency of offshore wind systems.
For example, Flores et al. used a multilayer perceptron (MLP) neural network to predict wind speed at one-hour intervals in Spain's wind farms [3]. The model, trained on data from two different locations, optimized power generation and ensured reliable energy sales. In another study, Dankert and Horstmann employed MLPs to retrieve wind speed and direction from radar images of the ocean surface [4]. This model showed promising results, accurately predicting wind speeds down to 0.5 m/s.
Further studies have focused on wave forecasting and optimal load reduction in wind farms. Researchers used support vector regression (SVR) and the Prony method to predict wave elevation and exciting forces, which play a critical role in turbine load management [5]. Another study by Kulkarni and Ghosh examined the potential impact of climate change on offshore wind energy by using downscaling techniques and General Circulation Models (GCMs) to forecast wind potential in India over the next few decades [6].
ML has also been applied to environmental monitoring, such as bird identification around offshore wind farms. Niemi and Tanttu developed a CNN-based system to identify bird species using a combination of image data and support vector machine (SVM) classifiers [7,8]. The system was enhanced with image augmentation to improve accuracy, even with limited data.
Yan et al. introduced an MLP-based model to predict wind farm power generation using data from the Lillgrund Wind Farm [9]. Their model achieved high accuracy and demonstrated the potential for transfer learning, allowing the model to be applied to other wind farms with similar turbine models. Meanwhile, Keivanpour et al. used neural network-based geo-clustering to assess offshore wind potential globally, identifying strategic deployment areas [10].
Finally, Zha et al. explored the use of reinforcement learning for path planning in wind farm areas, ensuring safe navigation of ships [11]. Additionally, Lin et al. employed unsupervised learning techniques to analyze underwater sound data, which could be used to assess the impact of noise on marine life near offshore wind farms [12].
This review highlights the transformative potential of machine learning in offshore wind energy, emphasizing its applications in environmental data prediction, system optimization, and health monitoring. As ML techniques continue to evolve, they offer new opportunities for enhancing the sustainability and efficiency of offshore wind farms. By further integrating these technologies into offshore energy systems, the industry can advance toward more environmentally responsible and economically viable renewable energy solutions.

Figure 1. Visualization and Information Retrieval from Offshore Wind Farm Soundscape
Machine Learning Applications in Offshore Wind Energy
This reviews various studies that utilize machine learning (ML) and other advanced techniques to assess and optimize the impacts of offshore wind energy development on marine ecosystems and improve the prediction of wind farm performance. The research incorporates diverse methodologies, such as random forests, artificial neural networks (ANN), clustering, and deep learning, to address challenges in marine spatial planning, fisheries, habitat mapping, and wind energy prediction.
1. Species Distribution Modeling for Marine Ecosystems
Researchers applied random forest models to NOAA’s long-term data to predict the distribution of 93 fish and macroinvertebrate species in the Northeast U.S. Continental Shelf. The model provided seasonal habitat distributions, helping to assess the potential impact of renewable wind energy installations in the Middle Atlantic Bight. The analysis highlighted that species with high biomass were primarily found in the northeast, while habitats supporting high occupancy were located further offshore.
2. Fisheries Impact Assessment Near Offshore Wind Farms
Stelzenmüller et al. used random forest regression to analyze the impact of offshore wind farms on passive gear fisheries. Their findings revealed that offshore wind farms attracted fishing activities due to their effect on brown crab populations. The spillover effects were particularly noticeable within 300 to 500 meters from turbines, underlining the complex relationship between wind farm locations and local fisheries.
3. High-Resolution Habitat Mapping for Marine Faunal Groups
Reijden et al. employed hierarchical clustering and random forest models to create high-resolution habitat maps of demersal fish, epifauna, and endobenthos in the North Sea. These maps help in assessing the anthropogenic pressures exerted by offshore wind farms, emphasizing the need for habitat maps in marine spatial planning to balance ecological and economic interests.
4. Wind Farm Performance Prediction Using Satellite Data
Tapoglou et al. proposed a hybrid model combining MLP networks and satellite remote sensing data (Sentinel-1 SAR images) to predict significant wave height and sea state in offshore wind farms. The model’s accuracy makes it suitable for operational use in offshore wind energy applications.

5. Offshore Wind Power Prediction with Data-Driven Models
Nguyen et al. developed data-driven models using CFD simulations to predict the aggregated power of offshore wind farms based on free-flow wind conditions. The study demonstrated how machine learning models, like decision trees and random forests, offer a more accurate prediction than traditional methods.
6. Bird Collision Risk Assessment with Wind Turbines
Mikami et al. created a fine-scale spatial model using random forests to predict bird collision risks with offshore wind turbines. Their research showed that the proximity to breeding colonies and harbors significantly influenced the likelihood of collisions, providing valuable data for mitigating risks to avian species.
7. Deep Learning for Offshore Wind Farm Detection
Hoeser et al. presented the DeepOWT dataset, which uses deep learning techniques to identify offshore wind farms from Sentinel-1 satellite imagery. This dataset aims to improve the monitoring and management of offshore wind infrastructure by making Earth observation data more accessible to stakeholders.
8. Offshore Wind Turbine Environment Monitoring Using SAR
Xu and colleagues introduced a random forest model in the Google Earth Engine to monitor the impact of offshore wind turbines on their surrounding environment using SAR images. Their model achieved high accuracy, making it suitable for dynamic surveillance of wind farm developments.
9. Noise and Interference Reduction in SAR Images
Xu also proposed a method to reduce the noise in SAR images of offshore wind turbines, using a combination of random forests and mathematical morphology techniques. This approach significantly improved the quality of time-series spatial data analysis, essential for monitoring offshore wind turbines' impact over time.
10. Prediction of Offshore Wind Power with Hybrid SVM Models
Yu and colleagues applied an SVM optimized with the dragonfly algorithm to predict ultra-short-term offshore wind power. Their model outperformed traditional optimization methods like particle swarm and firefly algorithms, highlighting its potential for improving wind energy forecasting accuracy.
These studies collectively illustrate the growing role of machine learning and satellite-based data in optimizing the development of offshore wind farms and minimizing their environmental impact. By integrating habitat modeling, fisheries impact assessment, collision risk prediction, and operational performance monitoring, these methodologies offer a comprehensive approach to managing the delicate balance between energy development and ecosystem preservation.

Figure 2. Process for Creating the DeepOWT Dataset Using Sentinel-1 Archive
Performance Modeling and Optimization in Wind Energy Generation
The importance of optimization in wind energy generation is emphasized by numerous research studies that have integrated neural networks into optimization processes, performance modeling, and controller design. A key challenge in this domain is the inherent variability of wind power output, which significantly impacts the stability of power grids. To address this, accurate ultra-short-term wind power prediction has become crucial for maintaining power system reliability. Other challenges include layout optimization, maximizing power generation, minimizing fatigue loads, and power reference tracking. A comprehensive review of the various control methodologies employed for these objectives can be found in [27].
In the realm of floating offshore wind turbines, these turbines present a promising economic opportunity, especially in deep water locations. However, their operation is subject to complex dynamics due to the interaction between wind and waves, which causes six-degree-of-freedom (DOF) movements. These interactions lead to oscillations in power output, mechanical loads, and the orientation of turbines, presenting operational challenges.
To address these challenges, Lee et al. developed an optimization algorithm that incorporated an MLP neural network to generate a wind and bathymetric map [28]. This algorithm aimed to identify the optimal location for an offshore wind farm near Jeju Island, South Korea. Using a genetic algorithm for optimization, the model found the best location based on maximum depth, distance from the coastline, and energy density.
Pappala et al. also employed a neural network in their optimization model for wind farm predictive control [29]. The model simulated an 80-turbine wind farm with each turbine rated at 50 MW, using past wind power generation data to predict the next three time steps. By utilizing particle swarm optimization, their model successfully reduced transformer tap changes and improved performance.
Japar et al. used data from the Horns Rev offshore wind farm in Denmark to develop machine learning models for estimating power losses caused by wake effects in wind farms [30]. The wake effect, which can lead to energy losses of up to 20% annually, necessitates careful consideration in wind farm layout optimization. The authors employed various machine learning techniques, including linear regression, MLP, and SVR, to estimate power losses, with SVR and MLP proving effective in this regard.
Antoniadou et al. applied neural network Gaussian processes to data from the Lillgrund Wind Farm to construct reference power curves for the 48 turbines in the farm [32]. These reference curves were used to predict the power output of other turbines, and the results demonstrated the robustness of the models, with consistently low MSE errors. The study utilized one full year of operational data, including various statistical measures for each 10-minute interval, to evaluate the predictive performance of each turbine.

Figure 3. Confusion matrix illustrating MSE errors on the testing set (left) and average MSE errors showing the predictive performance of each turbine's power output.
The extensive review of machine learning (ML) applications in offshore wind farms, highlighting various techniques and their effectiveness in optimizing different aspects of offshore wind farm operations. Below are some key points and insights from the studies discussed:
1. Reinforcement Learning for Network Control: Rodrigues et al. showcased how reinforcement learning techniques, specifically CARLA (Continuous Action Reinforcement Learning Automata), can be used for real-time optimization in multi-terminal DC networks. This method controls voltage in networks that connect offshore wind farms to onshore grids, improving power delivery stability.
2. Hybrid Multi-System for Offshore Wind and Wave Power Integration: Ou et al. proposed an intelligent damping controller combining a linear controller with adaptive critic networks and recurrent fuzzy neural networks to stabilize offshore wind and wave power systems, improving damping characteristics during unstable conditions.
3. ML for Wind Farm Production Prediction: Fischetti and Fraccaro explored the use of linear regression and multi-layer perceptron (MLP) networks to predict optimal production from offshore wind farms based on factors like turbine specifications and site characteristics. Their findings suggest that neural networks trained on optimized wind farm layouts can predict power production efficiently.
4. Damping Control for Offshore Wind Farms: Lu et al. combined fuzzy neural networks and genetic algorithms with particle swarm optimization for damping control in Static Synchronous Compensators connected to offshore wind farms. Their approach showed success in mitigating power oscillations and stabilizing the network.
5. Optimization in Floating Offshore Wind Turbines: Pillai et al. employed random forest-based surrogate models for multi-objective optimization of mooring systems in floating offshore wind turbines, balancing fatigue damage and material costs. Their approach provides useful trade-off information for decision-making.
6. Prediction of Wind Farm Power Output: Yin and Zhao utilized multiple ML algorithms, including regression neural networks and recurrent neural networks, to predict power output and thrust in offshore wind farms. Their methods showed up to 99% accuracy, demonstrating strong practical applications for wind farm optimization.
7. Deep Reinforcement Learning for Wind Farm Control: Dong et al. developed a deep reinforcement learning-based control scheme for optimizing power generation in wind farms. Their approach, which included a reward regularization module, improved the wind farm’s total power production by 15%, demonstrating the effectiveness of reinforcement learning in wind farm management.
8. Optimization of Wind Turbine Substructures: Häfele et al. used Gaussian process regression to optimize offshore wind turbine jacket substructures, reducing numerical expenses and considering more design load cases. This method allows for efficient handling of the computational challenges in offshore wind turbine design.
9. ML for Offshore Wind Farm Reliability and Power Generation: Miao et al. combined ML techniques like MLP networks and regression to analyze offshore wind farm reliability, considering factors such as climatic conditions and failure rates. This approach helps in evaluating power generation capacity and maintenance resource allocation.
10. Floating Wind Farm Control: Kheirabadi and Nagamune utilized distributed economic model predictive control (DEMPC) and MLP networks for optimizing floating offshore wind farms. Their system used ML to predict turbine states, enhancing the optimization process for floating platforms.
Key Trends:
• Hybrid Systems: Many of the approaches combine traditional control methods (e.g., PID controllers) with more advanced techniques like neural networks, genetic algorithms, and reinforcement learning.
• Optimization: There is a strong emphasis on optimization, whether for predicting power output, controlling damping, or minimizing costs and damage.
• Data-Driven Models: Most studies rely heavily on data, including high-fidelity simulations and real-world operational data, to train and validate ML models for wind farm control and optimization.
• Adaptability: Several studies highlight the adaptability of ML approaches, particularly reinforcement learning, to handle the uncertainties and dynamic nature of offshore wind farm operations.
These findings point to the growing role of ML in enhancing the efficiency and stability of offshore wind farms, from control and optimization to maintenance and reliability assessments. The integration of these methods into wind farm operations could lead to significant advancements in energy generation, stability, and cost-effectiveness.
Wind energy has emerged as one of the most promising and clean sources of renewable energy. With increasing global demand for sustainable and eco-friendly energy sources, wind power has gained considerable traction. Offshore wind energy, in particular, offers significant potential due to its ability to harness stronger and more consistent wind patterns at sea.
Challenges in Offshore Wind Energy Harvesting
Offshore wind farms face numerous challenges, including high operational and maintenance costs, turbulent wind conditions, and the complexity of maximizing energy capture. The variability in wind speeds and directions also poses a challenge to optimal energy production.
The Role of AI in Wind Energy
Artificial Intelligence (AI) has demonstrated immense potential in optimizing various aspects of renewable energy systems. AI can be applied to predict and manage energy production, improve efficiency, and reduce downtime in offshore wind farms. By utilizing machine learning algorithms, AI can help to predict wind patterns, optimize turbine operations, and address unforeseen operational issues in real time.

Conceptual Framework of AI-Optimized Wind Energy Harvesting
What is an AI-Optimized Wind Energy Harvesting System?
An AI-optimized wind energy harvesting system refers to the integration of AI and machine learning technologies with wind turbines to enhance energy capture and maximize offshore power generation. The system uses real-time data collected from sensors and weather forecasts to optimize turbine operations, reduce wear and tear, and ensure maximum efficiency in energy harvesting.
AI Algorithms in Wind Energy Optimization
AI systems utilize various machine learning algorithms, including supervised learning, reinforcement learning, and neural networks, to process vast amounts of data and derive insights. These algorithms are capable of predicting the wind speed, direction, and turbine performance, thus optimizing the turbine’s pitch and yaw settings for maximum energy generation.
Offshore Wind Energy Systems
Components of Offshore Wind Farms
Offshore wind farms typically consist of several key components:
• Wind Turbines: The main units for energy conversion, which are installed on floating or fixed platforms in the sea.
• Platforms/Foundations: These provide structural support for the turbines and are designed to withstand harsh marine environments.
• Energy Storage and Transmission Systems: These systems store and transmit the energy generated by the turbines to shore.
Floating vs. Fixed Offshore Wind Turbines
Offshore wind turbines can be divided into two main categories:
• Fixed-bottom turbines: Installed in shallow waters and fixed to the seabed.
• Floating turbines: Installed in deeper waters, using floating platforms that are tethered to the ocean floor.
Floating offshore wind farms offer flexibility and have the potential to generate more energy as they can be placed further offshore in locations where wind speeds are higher.
Role of AI in Maximizing Offshore Power Generation
AI-Driven Prediction of Wind Conditions
AI plays a pivotal role in predicting wind conditions using advanced forecasting models. By utilizing machine learning, AI systems can analyze historical wind data, satellite imagery, and weather patterns to predict upcoming wind speeds and directions. These forecasts allow offshore wind turbines to adjust their operations to capture maximum wind energy, even under fluctuating conditions.
Real-time Monitoring and Adjustment
AI systems can continuously monitor wind turbine performance through an array of sensors that measure factors such as wind speed, vibration, temperature, and rotor speed. With this real-time data, AI algorithms can dynamically adjust turbine parameters, such as blade pitch and yaw, to optimize energy capture. This constant adjustment ensures that the turbines operate at peak efficiency, minimizing energy loss.
Predictive Maintenance and Fault Detection
AI-based predictive maintenance models can detect faults before they occur by analyzing sensor data for patterns that may indicate wear or failure. By identifying potential issues early, these systems allow for preventative maintenance, reducing downtime and repair costs.
Optimizing Energy Storage and Transmission
AI can also be applied to optimize energy storage systems. By predicting energy production levels, AI can manage the flow of energy from turbines to storage and transmission systems. This ensures that excess energy is efficiently stored and that transmission to the grid is maximized.
Data Acquisition and Integration
Sensors and IoT Devices
The success of an AI-optimized wind energy harvesting system heavily relies on the data collected from various sensors and IoT devices. These sensors track turbine performance, environmental conditions, and even marine data (such as wave height and salinity). Data acquisition in real-time provides the information needed for AI algorithms to make instantaneous adjustments.
Data Fusion and AI Integration
Data from various sources, such as meteorological stations, ocean buoys, and turbine sensors, need to be integrated into a single platform for analysis. AI systems utilize data fusion techniques to merge disparate datasets, enabling a holistic view of the wind farm’s performance.
Big Data Analytics in Wind Energy
The ability to process and analyze big data in wind energy is crucial. AI algorithms, powered by large datasets, can uncover hidden patterns and correlations, enhancing the overall efficiency of the system. Big data analytics allows wind energy operators to make informed decisions on turbine operation and maintenance strategies.
Benefits of AI-Optimized Offshore Wind Energy Systems
Increased Energy Efficiency
AI can significantly improve the efficiency of offshore wind farms by ensuring that turbines are constantly adjusted to optimize energy capture. By predicting and responding to environmental conditions, AI ensures that turbines perform at their best.

Cost Reduction
AI-driven optimization can help reduce operational and maintenance costs by predicting and preventing equipment failures. Furthermore, energy generation is optimized, leading to higher returns on investment. AI’s role in streamlining operations can also reduce the need for manual intervention.
Enhanced Reliability and Stability
By continuously monitoring and adjusting turbine operations, AI helps ensure the reliability and stability of power generation. In addition, predictive models minimize downtime, ensuring that turbines are running at peak performance as much as possible.
Scalability of Offshore Wind Farms
AI-optimized systems make it easier to scale up offshore wind farms by automating operations, making it feasible to manage larger farms with greater efficiency. This scalability is critical as global demand for renewable energy increases.
Case Studies and Examples of AI in Offshore Wind Energy
Case Study: DeepWind Offshore Wind Farm
A notable example of AI optimization in offshore wind energy is the DeepWind offshore project, where AI was used to predict wind conditions and adjust turbine operations in real time. The system demonstrated significant improvements in energy capture, even in areas with irregular wind patterns.
Case Study: Siemens Gamesa and AI in Wind Turbine Optimization
Siemens Gamesa has been applying AI for predictive maintenance and optimization in its offshore wind turbines. By integrating AI into their turbines, they’ve been able to improve operational efficiency and reduce downtime, providing a real-world example of AI’s impact on offshore wind energy.

Future Prospects and Challenges
Challenges in AI-Optimized Offshore Wind Systems
Despite the potential benefits, there are challenges in implementing AI systems in offshore wind energy, such as the complexity of integrating AI with existing infrastructure, data security concerns, and the high initial costs of AI technology.
The Future of AI in Offshore Wind Energy
As AI continues to evolve, its application in offshore wind energy will become more advanced. Future advancements include autonomous systems for turbine operation, more sophisticated predictive models, and improved machine learning algorithms for energy optimization.
Conclusion
The integration of AI in offshore wind energy systems presents a promising solution for maximizing power generation, reducing operational costs, and enhancing system reliability. As AI technology evolves, it will play an even more critical role in optimizing wind energy harvesting, contributing significantly to the global transition to renewable energy.

DETAILED DESCRIPTION OF DIAGRAM
Figure 1. Visualization and Information Retrieval from Offshore Wind Farm Soundscape
Figure 2. Process for Creating the DeepOWT Dataset Using Sentinel-1 Archive
Figure 3. Confusion matrix illustrating MSE errors on the testing set (left) and average MSE errors showing the predictive performance of each turbine's power output. , Claims:1. Ai-optimized wind energy harvesting system for maximizing offshore power generation claims that the integration of AI algorithms allows for real-time prediction and optimization of wind turbine performance, maximizing energy capture by adjusting turbine settings based on varying wind conditions.
2. AI-driven systems enable dynamic monitoring of environmental factors such as wind speed, direction, and turbulence, ensuring turbines operate at peak efficiency in diverse offshore conditions.
3. Machine learning models can predict wind patterns and turbine performance, allowing for proactive adjustments that optimize power generation and prevent energy loss.
4. AI enhances predictive maintenance capabilities by analyzing sensor data for early detection of potential faults or inefficiencies, reducing downtime and maintenance costs.
5. By integrating data from various sources such as weather forecasts, satellite images, and turbine sensors, AI systems enable real-time decision-making that boosts the overall reliability and stability of offshore wind farms.
6. AI algorithms help in optimizing energy storage and transmission by predicting fluctuations in power generation, ensuring efficient storage of excess energy and stable grid integration.
7. The application of AI makes offshore wind energy harvesting more scalable, enabling better management of larger wind farms and improving the economic viability of offshore wind projects.