Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review

Abstract

This literature review critically examines the development and optimization of sustainable energy and exergy analysis software specifically designed for offshore wind farms, emphasizing the transformative role of machine learning (ML) in overcoming operational challenges. Offshore wind energy represents a cornerstone in the global transition to low-carbon economies due to its scalability and superior energy yields; however, its complex operational environment, characterized by harsh marine conditions and logistical constraints, necessitates innovative analytical tools. Traditional deterministic methods often fail to capture the dynamic interactions within wind farms, thereby underscoring the need for ML-integrated approaches that enhance precision in energy forecasting, fault detection, and exergy analysis. This PRISMA-ScR review synthesizes recent advancements in ML techniques, including Random Forest, Long Short-Term Memory networks, and hybrid models, demonstrating significant improvements in predictive accuracy and operational efficiency. In addition, it critically identifies current gaps in existing software tools, such as inadequate real-time data processing and limited user interface design, which hinder the practical implementation of ML solutions. By integrating theoretical insights with empirical evidence, this study proposes a unified framework that leverages ML algorithms to optimize turbine performance, reduce maintenance costs, and minimize environmental impacts. Emerging trends, such as incorporating digital twins and Internet of Things (IoT) technologies, further enhance the potential for real-time system monitoring and adaptive control. Overall, this review provides a comprehensive roadmap for the next generation of software tools to revolutionize offshore wind farm management, thereby aligning technological innovation with global renewable energy targets and sustainable development goals.

1. Introduction

The global shift toward renewable energy has become essential due to the increasing urgency of climate change mitigation and the pursuit of sustainable development goals. Traditional dependence on fossil fuels has led to significant environmental degradation and unprecedented greenhouse gas emissions, intensifying the need for cleaner and more sustainable energy alternatives [1].

Among these alternatives, wind energy has emerged as one of the most promising solutions, thanks to its scalability, abundance, and rapidly decreasing costs. Offshore wind energy has gained considerable attention as a crucial component of the renewable energy transition. Its advantages include higher and more consistent wind speeds, reduced visual and noise pollution compared to onshore installations, and ample space for large-scale developments. These features enable offshore wind farms to produce higher energy yields, making them a key resource for sustainably meeting national and global energy demands [2].

Despite its considerable potential, offshore wind energy encounters many challenges related to operation and maintenance. The harsh marine environment, corrosive saltwater exposure, severe weather events, and logistical hurdles make optimizing offshore wind farms complex and expensive. These factors require sophisticated solutions to ensure economic sustainability while maintaining operational efficiency [3,4].

Traditional performance analysis methods have proved inadequate in capturing the complex dynamics of offshore environments, often failing to provide actionable insights into energy and exergy efficiencies. This limitation underscores the urgent need for advanced analytical tools that can address inefficiencies and enhance the overall performance of wind farms.

On the other hand, energy and exergy analyses have become essential methodologies for evaluating and improving the efficiency of energy systems, particularly offshore wind farms. Energy analysis measures the total energy produced and consumed, providing a general overview of system performance. However, it does not consider the quality or usability of the generated energy, which is where exergy analysis plays a crucial role. Exergy analysis offers a more comprehensive evaluation by assessing the quality of energy and identifying the locations and modes of energy losses. This method enables operators to detect inefficiencies and focus on specific areas for enhancement, thereby improving the overall efficiency of wind farms [5]. By integrating exergy analysis, operators gain a deeper understanding of system behavior, which is vital for achieving sustainable energy systems that are aligned with climate change mitigation goals.

In recent years, the adoption of machine learning (ML) algorithms has revolutionized how energy and exergy analyses are performed. ML methods, including Random Forest (RF), Long Short-Term Memory (LSTM) networks, and Multiple Linear Regression (MLR), have shown impressive capabilities in managing complex systems and forecasting performance outcomes. These algorithms enable more accurate predictions of wind farm behavior, aid in fault detection and predictive maintenance, and optimize energy production in real time. For example, ML models can effectively represent non-linear dependencies between variables, prioritize features, and capture short-term temporal patterns in time-series data [6,7].

On the other hand, recent studies indicate that integrating machine learning with digital twin frameworks enhances predictive maintenance accuracy and allows for the simulation of intricate operational scenarios in a risk-free environment [6]. This emerging paradigm is set to transform the offshore wind industry by markedly decreasing downtime and augmenting system resilience.

This predictive capability is crucial for optimizing offshore wind farm operations because it enables operators to make informed decisions based on accurate forecasts and actionable insights. However, the real-world use of ML algorithms in offshore wind farms is still limited due to the lack of specialized software tools. Current tools often fail to integrate state-of-the-art ML algorithms or conduct comprehensive energy and exergy analyses. This gap in software capabilities hinders operators’ ability to fully leverage the potential of ML, leading to missed opportunities for optimization and increased operational costs. Moreover, the lack of user-friendly interfaces and integrated pipelines for data acquisition, preprocessing, and real-time performance assessment complicates the implementation of ML-based solutions in practical scenarios [8,9].

The operational and analytical challenges require advanced software tools that combine machine learning (ML) algorithms with energy and exergy analyses. Such tools can enhance predictive accuracy, optimize turbine performance, and facilitate real-time decision-making in the dynamic offshore environment. This study aims to bridge the gap between theoretical advancements in ML and their practical application in offshore wind energy systems by systematically reviewing recent developments in ML-integrated software tools. Specifically, it categorizes advancements in predictive modeling, optimization techniques, and fault detection achieved through ML algorithms such as Random Forest, Long Short-Term Memory (LSTM) networks, and hybrid models. Additionally, it evaluates the limitations of existing software tools, including inadequate real-time data processing and user interface design, to propose a unified framework for next-generation offshore wind farm management.

This research holds great importance because it offers solutions to improve offshore wind farms’ operational efficiency and sustainability, which supports worldwide renewable energy targets. This study synthesizes theoretical knowledge with real-world data to provide practical recommendations for wind farm optimization, cost reduction, and environmental protection. Offshore wind farm operators, policymakers, researchers, and software developers who work on ML applications for renewable energy form the target audience of this study.

This paper has the following structure: Section 2 explains the PRISMA-ScR methodology that guided the systematic literature review process. Section 3 presents the findings, categorized by ML applications, software tools, and simulation models. Section 4 discusses the implications of these findings, addressing challenges and future directions. Finally, Section 5 summarizes the key conclusions and provides recommendations for future research.

2. Methods

2.1. Search Strategy

Systematic searches of major academic databases, including Scopus and Web of Science (WoS), were conducted in the review process to capture the maximum number of relevant studies. The search was restricted mainly to publications written between January 2019 and October 2024, including the latest progress. However, original works with many citations and lasting impact were also included in the present paper when relevant to the early years.

Keywords used in the search included “ML in renewable energy”, “energy and exergy analysis software”, “Offshore wind optimization”, “Simulation models for offshore wind”, and “ML in offshore wind energy”. Binary operators and reductions were utilized to enhance the results, balancing comprehensiveness and specificity.

2.2. Inclusion and Exclusion Criteria

Inclusion and exclusion criteria were carefully established to concentrate on studies directly contributing to ML applications in offshore wind energy.

i. 

Inclusion criteria:

-

Studies published in peer-reviewed journals or conference proceedings.

-

Publications primarily focus on ML applications in offshore wind energy systems, emphasizing energy and exergy analyses, predictive maintenance, environmental impacts, wind farm design optimization, and ML model performance.

-

Works demonstrating empirical results, simulations, or case studies.

ii. 

Exclusion criteria:

-

Non-English-language studies.

-

Articles with limited focus on ML or offshore wind applications in abstract screening.

-

Review articles, editorials, and opinion pieces lacking original research contributions in full-text screening.

iii. 

Selection Process

The PRISMA framework structured the selection process, as shown in Figure 1 and as outlined below:

a. 

Identification: A comprehensive search of Scopus and Web of Science (last updated 15 October 2024) identified 403 records relevant to offshore wind ML applications. After removing duplicates (n = 113), 290 unique records remained for further consideration (see Figure 1).

b. 

Screening: Titles and abstracts of the 290 unique records were screened independently by two reviewers against the inclusion criteria. At this stage, 114 records were excluded for not meeting key criteria—for example, they were off-topic, not in English, or not the required research publication type. Disagreements between the two reviewers were resolved through discussion and consensus, with a third reviewer consulted if necessary. This screening process left 176 records deemed potentially eligible. Supplementary full-text articles were excluded, providing a representative sample of the studies excluded during the full-text screening stage and the primary reasons for their exclusion.

c. 

Eligibility: The full text of each of the 176 remaining articles was then examined in detail by the same two reviewers, working independently to apply all eligibility criteria. Upon full-text review, 122 studies were further excluded: 92 were found to lack sufficient relevance or focus on the review topic upon closer examination, and another 30 exhibited methodological weaknesses or insufficient empirical rigor. Any conflicts in study inclusion decisions at this stage were resolved by consensus or, if needed, by involving a third reviewer. This rigorous eligibility assessment left 59 studies fully meeting the inclusion criteria.

d. 

Inclusion: Overall, 64 peer-reviewed studies were included in the final qualitative synthesis of the systematic review (i.e., these 54 studies underwent data extraction, analysis, and risk-of-bias appraisal). In addition, the reviewers identified six relevant reports from recognized industry and government unions to provide supplementary context on the topic. Importantly, these six union reports were treated as supporting background material—they were not included in the formal systematic review synthesis or the risk-of-bias assessment. The final set of peer-reviewed studies (64) forms the basis of the results reported, in line with the PRISMA 2020 flow diagram (Figure 1) and the abstract review.

2.3. Risk of Bias Assessment

A thorough risk of bias assessment was undertaken for each included study using a customized appraisal framework to ensure the reliability and validity of the review’s findings. This framework was developed in line with the methodological recommendations outlined in the PRISMA 2020 guidelines [11], and focused on critical domains such as study design, participant selection, data collection methods, confounding factors, and reporting transparency. Two independent reviewers conducted the assessments, and any discrepancies were resolved through consensus or the involvement of a third reviewer. The insights gained from these evaluations were integrated into the narrative synthesis and discussion, informing the interpretation of the overall evidence quality and highlighting potential limitations. No formal assessment of reporting bias (e.g., publication bias) was conducted, as the synthesis was qualitative and based solely on published studies.

2.4. Synthesis of Results

Due to the heterogeneity of the included studies, the results were synthesized using a narrative approach. Data were categorized based on the type of ML application, and key findings were summarized qualitatively. Where possible, quantitative data were extracted and presented in tables to highlight trends and patterns across studies.

2.5. Protocol and Registration

No prior protocol was registered for this systematic review. The review was not prospectively registered in PROSPERO or any other registry, and no separate published protocol exists. The methodology was developed and reported here in adherence to PRISMA 2020 guidelines.

3. Results

3.1. Overview of Included Studies

The SLR generated 64 studies, 8 reports, 4 application websites, and 1 government site to be included in the final analysis. These studies were clustered so that several challenges concerning the combination of ML in offshore wind power systems were included. The categories identified are as follows:

• ML applications in energy and exergy analysis (19 studies);
• Predictive maintenance and fault detection (9 studies);
• Environmental impact assessments (14 studies);
• Optimization of wind farm layout and performance (26 studies);
• Hybrid ML models and deep learning applications (8 studies).

Table 1. Classification of references by category.

Category	Description	References
ML Applications in Energy and Exergy Analysis	Focus on utilizing ML algorithms to optimize energy output and conduct exergy analysis in offshore wind farms, emphasizing improving energy efficiency.	[5,6,8,9,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]
Predictive Maintenance and Fault Detection	Exploration of ML techniques for predictive maintenance, fault detection, and condition monitoring in wind turbines, enhancing reliability and reducing costs.	[4,27,28,29,30,31,32,33,34]
Environmental Impact Assessments	Assessment of environmental impacts of offshore wind farms using ML, including ecological risks and sustainability considerations.	[1,35,36,37,38,39,40,41,42,43,44,45,46,47]
Optimization of Wind Farm Layout and Performance	Studies on optimizing wind farm design, layout, and operational performance using simulation models and computational methods.	[2,3,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71]
Hybrid ML Models and Deep Learning Applications	Implementation of hybrid ML models and deep learning techniques to improve wind energy systems’ forecasting accuracy and operational efficiency.	[7,72,73,74,75,76,77,78]

Note: Reference numbers correspond to the numbered list in the References section.

presents these categories along with their corresponding publications. This taxonomy provides a cohesive framework for the current state of research and emphasizes the limited number of research domains and questions in the field.

By categorizing references as outlined above, the table enhances comprehension of the literature review; specifically, some studies related to certain aspects of ML applications in offshore wind energy can be readily identified. It is a visual tool that complements textual analysis and summarizes the research domain’s scope.

3.2. Synthesis of Results

The narrative synthesis showed that ML applications deliver substantial benefits for both predictive accuracy and operational efficiency in offshore wind farms. Research using Random Forest and LSTM networks demonstrated that their methods delivered 15% better energy forecasting accuracy than conventional approaches. Research studies identified data quality issues and model interpretability problems as recurring challenges. The qualitative interpretation of findings received additional support through the summary of study characteristics and bias risk assessment for each thematic synthesis.

3.3. Key Findings

i. 

Energy and Exergy Optimization: ML methods and intense learning have significantly enhanced the accuracy of power output predictions, making the intelligent management of offshore wind inventories feasible. The present studies also report applications of an ML algorithm for energy forecasting and exergy analysis, demonstrating improvements in wind farms’ efficient and environmentally friendly operation [6,9].

ii. 

Enhanced Predictive Maintenance: Support vector machines and clustering algorithms are among the ML models that have successfully predicted future turbine failures and supported proactive maintenance strategies. For instance, Turnbull et al. applied support vector machines to analyze vibration data for bearing failure prognosis, while Lützen and Beji examined clustering techniques for the preventive maintenance of an offshore wind turbine. These methods reduce time and operational costs by enabling proactive time measures [4,29]. Moreover, sensor technologies and considerable data analytics enhancements have significantly elevated data quality for ML applications. Incorporating high-fidelity sensors with real-time data processing has improved the reliability of prediction models, allowing adaptive control systems that can dynamically adjust to environmental changes [9].

iii. 

Sustainability and Environmental Assessment: ML-based impact assessments have identified potential ecological risks linked to offshore wind farms, supporting the sustainability vision. Research such as that by Bailey et al. explores environmental impacts and offers suggestions for future advancements. The sustainability vision receives support from ML-based impact assessments that detect ecological risks in offshore wind farms. The study by Bailey et al. investigates environmental effects while recommending upcoming developments. The DeepOWT dataset creation process is shown in Figure 2, and uses Sentinel-1 radar data to map global offshore wind turbines [9]. The two-step deep learning object detection system allows stakeholders to monitor wind farm growth through turbine location identification. The open access nature of the dataset allows more stakeholders to participate in marine spatial planning [9].

Copping et al. have examined risk management and consent procedures for marine renewable energy utilization. These studies are crucial for minimizing environmental footprints and meeting compliance with regulatory standards [27,35]. An ML-driven lifecycle assessment (LCA) framework could unify environmental and economic metrics, evaluating trade-offs between carbon emissions, biodiversity impacts, and levelized cost of energy (LCOE). This would inform holistic sustainability strategies for offshore projects.

iv. 

Optimization of Wind Farm Design and Performance: Advanced computing tools and simulation methods have improved wind farm layouts and operational parameters, increasing efficiency and net power production. Fischetti and Fraccaro also refined ML to its full potential, while software applications, such as DNV’s WindFarmer (Available at: https://store.veracity.com/windfarmer-analyst-license, Accessed: 5 October 2024), provide effective environments for developing performance models and optimization strategies. These tools and methods facilitate informed design decisions and enhance overall performance [14,51].

v. 

Integration of Hybrid Models and Deep Learning: Integrating hybrid approaches that combine ML models with deep learning has improved forecast quality and operational efficiency, effectively responding to the highly dynamic nature of offshore wind conditions. Studies such as that by Liu et al. focus on deep learning paradigms for wind speed forecasting, while Stetco et al. explore hybrid approaches for condition monitoring [31]. The Deep Neural Network (DNN), featuring two hidden layers with 100 neurons each, forecasts wake effects by creating a velocity grid in offshore wind farm simulations. This model incorporates batch normalization, linear activation functions, and FLORIS’s 200 × 200 grid, allowing for parallel sub-networks that enhance resolution, as shown in Figure 3 [77].

State-of-the-art (SOTA) ensemble learning techniques that integrate with agent-oriented systems have demonstrated promising results for offshore wind energy applications. The ensemble learning frameworks of Soygazi (2023) demonstrated decentralized decision-making for turbine control and fault detection in complex wind farm environments through the combination of multiple ML models with agent-based coordination [24]. Agent-oriented systems coordinate turbine interactions to optimize overall farm performance by utilizing the strengths of individual models, such as Random Forest for feature prioritization and LSTM for temporal dependencies. The SOTA techniques improve the robustness and scalability of ML applications, especially for large-scale offshore wind farms operating under dynamic marine conditions [24]. By integrating multiple algorithms, wind energy systems’ predictive potential and flexibility are enhanced [31,72].

This classification highlights the broad spectrum of ML applications for offshore wind energy systems. The emphasis on energy and exergy optimization, predictive maintenance, and performance enhancement is symptomatic of the industry’s focus on efficiency, reliability, and sustainability improvement efforts. Contributing studies varied in design, predominantly consisting of simulation-based analyses supplemented by a few real-world evaluations, with most assessed as having low to moderate risk of bias. By using ML techniques, researchers and practitioners are in a position to address the issues arising during the operation of offshore wind farms in greater complexity, thus leading to their designation as renewable sources of energy technologies.

Table 2. Summary of key findings from the literature review.

Key Finding	Description	Representative Studies	Outcome/Impact
Energy and Exergy Optimization	ML improves power output predictions and exergy analysis.	[6,9]	Enhanced forecasting and energy management.
Enhanced Predictive Maintenance	ML predicts turbine failures; improved data quality aids in adaptive control.	[4,9,29]	Reduced downtime and lower maintenance costs
Sustainability and Environmental Assessment	ML assesses ecological risks to support sustainability.	[27,35]	Lower environmental impact and better compliance.
Wind Farm Design and Performance Optimization	Advanced tools optimize layouts and operational parameters.	[14,61]	Optimized design and increased net power production.
Integration of Hybrid Models and Deep Learning	Combining ML with deep learning enhances forecasting under dynamic conditions.	[31,72]	Improved adaptability and predictive performance.

concisely summarizes the key findings from the literature review, emphasizing the main ML applications in offshore wind farms and their associated impacts on performance and sustainability maintenance. Risk of bias observations related to each group of studies were considered during synthesis, particularly in interpreting the strength of evidence where methodological limitations were present.

3.4. Comparative Analysis of ML Models

This paper establishes a method for evaluating the benefits and weaknesses of ML models applied to offshore wind systems by comparing six algorithms—Random Forest (RF), Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), XGBoost, LightGBM, and Hybrid Models—and using mean absolute error (MAE) and root mean square error (RMSE) as performance metrics. The data are taken from multiple studies of A. Alkesaiberi et al. (2022) [76] on wind power forecasting for France, Turkey, and Kaggle datasets, ref. [25] on LSTM for wind power forecasting with normalized metrics, M. L. Hossain et al. (2024) [26] on SVM for wind speed prediction, G. Liu et al. (2022) [78] on hybrid models for wind speed forecasting, and F. Soygazi (2023) [24] on tree-based models (Random Forest, XGBoost, LightGBM) with different data split ratios for wind power prediction.

Notably, F. Soygazi (2023) [24] gives the updated MAE and RMSE ranges from the performance graphs, with Random Forest having an MAE between 165 and 215 kW and RMSE between 345 and 460 kW, XGBoost having an MAE between 160 and 200 kW and RMSE between 355 and 390 kW, and LightGBM having an MAE between 160 and 190 kW and RMSE between 345 and 360 kW. LSTM models, as reported in research by Ö. A. Karaman (2023) [25], have very low normalized MAE and RMSE values, showing high accuracy in time series forecasting. SVM works well for wind speed (0.67 m/s in [26]) and power forecasting (e.g., MAE = 126.07 kW in [76]). Bagged Trees and LGB-GPR are the two hybrid models that are employed for wind power forecasting (MAE = 124.33 kW in [76]) and wind speed probabilistic forecasting (MAPE = 0.133 in [78]), respectively. The DecisionTreeRegressor, evaluated in the study by F. Soygazi [24], was not included in this analysis because it performed poorly compared to the selected models, with an MAE of 195–290 kW and RMSE of 400–620 kW. The comparison indicates that the LightGBM tree-based model exhibits the best potential for the stable and accurate prediction of offshore wind energy.

Table 3. Comparison of machine learning models for offshore wind energy applications.

Model	Application	MAE	RMSE	Units	Strengths	Limitations	Source
Random Forest (RF)	Wind Power Forecasting	165–215	345–460	kW	Robust to noise, handles non-linear data well	Limited temporal dependency modeling	[24]
LSTM Networks	Wind Power Forecasting	0.0209	0.0614	(normalized)	Captures temporal dependencies effectively	Computationally intensive requires extensive data	[25]
Support Vector Machines (SVM)	Wind Speed Prediction	0.67	0.67	m/s			[26]
	Wind Power Forecasting	126.07 (France), 70.01 (Turkey), 74.88 (Kaggle)	185.50 (France), 111.48 (Turkey), 123.43 (Kaggle)	kW	Effective for high-dimensional data	Sensitive to hyperparameter tuning	[76]
XGBoost	Wind Power Forecasting	160–200	355–390	kW	High accuracy, handles large datasets	Computationally intensive, requires tuning	[24]
LightGBM	Wind Power Forecasting	160–190	345–360	kW	Fast training, efficient memory usage	Less interpretable than other tree-based models	[24]
Hybrid Models (Bagged Trees)	Wind Power Forecasting	124.33	183.66	kW	Combines the strengths of multiple models.	High computational complexity	[76]
Hybrid Models (LGB-GPR)	Wind Speed Probabilistic Forecasting	MAPE = 0.133	RMSE = 0.288	-			[78]

Note: For [24], MAE and RMSE values are ranges based on varying “common_unique_ratio” (0.1 to 0.9). For LSTM [25], metrics are likely normalized due to their small values relative to typical wind power units. DecisionTreeRegressor was evaluated in [24] but excluded due to its higher MAE (195–290 kW) and RMSE (400–620 kW), indicating poorer performance.

provides the strengths and limitations of each machine learning approach.

3.5. Research Gaps and Challenges

I. 

Data Quality and Availability: Although ML models excel with large datasets, access to high-quality, real-time data still needs to be improved [73]. Machine learning models require standardized data acquisition and preprocessing protocols to achieve reliable and accurate results in offshore wind farm applications. Using internationally recognized standards like IEC 61400-25 for wind turbine communication and data exchange [79], enables consistent data collection across meteorological, operational, and environmental datasets. An open access database for offshore wind farm data would enhance data sharing between researchers and practitioners, leading to better data quality and availability. The database would function as a central repository to develop stronger ML models with generalizable results while addressing the data noise issues commonly occurring in offshore environments. Improved data sharing and standardization across stakeholders is essential.

II. 

Model Interpretability and Transparency: Complex ML models, with intense learning, often lack interpretability and challenge industry adoption [74]. The increasing complexity of machine learning models, especially deep learning architectures, requires greater transparency and interpretability in their decision-making operations. The ML pipeline can integrate XAI frameworks to explain model prediction processes, including Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive explanations (SHAP). SHAP values help determine which turbine features, like vibration levels and temperature readings, impact prediction outcomes most in failure prediction tasks. Implementing XAI techniques through case studies enables stakeholders to better understand model behavior, which builds trust and supports ML-based solution adoption in offshore wind farm management.

III. 

Integration with Operational Systems: Ensuring interoperability with existing wind farm management systems is crucial. The development of an open source modular machine learning platform should be pursued to overcome current proprietary software tool limitations. WindML represents a proposed platform that integrates Python (available at: https://www.python.org, Accessed: 10 October 2024) to work harmoniously with OpenFAST (available at: https://openfast.readthedocs.io, Accessed: 15 October 2024) and WindFarmer (Available at: https://store.veracity.com/windfarmer-analyst-license, Accessed: 5 October 2024) tools. WindML would feature a modular structure that enables users to add new ML algorithms and modify the software according to their project needs. The open-source nature of this initiative would foster community involvement and collaboration, which would speed up innovation while making it easier for small organizations and researchers to participate. Developing modular architectures that support incremental ML component integration is essential [75].

Having systematically reviewed and categorized the current literature on ML applications in offshore wind energy systems, it becomes clear that integrating these advanced technologies has significant potential for optimizing performance and sustainability. However, to fully understand these findings’ implications and practical applications, it is essential to establish a comprehensive grasp of the offshore wind energy sector itself.

Therefore, the following section provides an in-depth overview of offshore wind energy, exploring its historical development, technological advancements, operational dynamics, and unique challenges. This foundational knowledge will contextualize the subsequent discussions on how ML techniques can be effectively leveraged to enhance offshore wind farm operations, aligning with the overarching objectives of this research.

3.6. Offshore Wind Energy Overview

A. 

Development and Growth

Offshore wind energy has become a crucial part of the global shift toward renewable energy sources, providing substantial potential for large-scale electricity generation. The offshore wind sector has seen significant growth over the past decade, fueled by technological innovations, supportive policies, and rising demand for clean energy. According to the Global Wind Energy Council (GWEC), global offshore wind capacity reached 35.3 GW by the end of 2020, marking a 29% increase from the previous year [53]. Figure 4 shows the average installed capacity of new wind turbines in Europe (in megawatts).

Projections indicate a dramatic increase in offshore wind farms over the coming years, as illustrated in Figure 5.

This anticipated growth highlights the urgency of using advanced optimization techniques to effectively manage the expanding offshore wind infrastructure.

Expert surveys predict a significant increase in land-based and offshore wind turbine sizes, which would enhance energy capture and efficiency, as shown in Figure 6. This advancement in turbine technology requires sophisticated analytical tools to optimize performance and address related operational challenges.

B. 

Technological Advancements

Technological innovations have played a crucial role in enhancing the efficiency and feasibility of offshore wind energy. The trend toward developing increasingly larger turbines has been significant. For instance, in 2020, Siemens Gamesa deployed a 14 MW offshore wind turbine with a 222 m rotor, resulting in increased output [80].

Recent advancements in floating wind turbine technology have facilitated the installation of offshore wind farms in deeper waters, thereby expanding their geographical reach. Floating platforms allow for installations in water depths exceeding 60 m, going beyond the limits of fixed-bottom turbines. This development provides a new outlook for the offshore wind industry, particularly in areas where coastal waters are deep.

C. 

Economic Implications

Offshore wind has significant economic ripple effects, affecting employment, supply chains, GDP growth, etc. According to the International Renewable Energy Agency (IRENA), it is projected to exceed 2 million jobs by 2030 based on current trends, as the offshore wind sector is expected to employ approximately 700,000 people worldwide in 2020, increasing to over 2 million by 2030 [38]. Furthermore, this is supported by the fact that the cost of offshore wind power has continuously decreased as competition has grown. Concurrently, offshore wind’s levelized cost of electricity (LCOE) dropped 48% between 2010 and 2020, making electricity production increasingly attractive economically [38].

D. 

Environmental Considerations

Although offshore wind energy provides significant environmental benefits by decreasing fossil fuel consumption, it also poses challenges that need careful management, such as potential impacts on marine ecosystems, including effects on bird and marine mammal populations [35]. However, research suggests that these impacts can be minimized by thoughtfully selecting sites and applying appropriate mitigation strategies [27]. Additionally, offshore wind turbines can create habitats, as their foundations can serve as artificial reef structures, enhancing site biodiversity [39].

E. 

Policy and Regulatory Frameworks

Provisional policies and regulatory measures have been crucial to developing the offshore wind energy industry. Governments have various tools to encourage investment, including feed-in tariffs, auctions, and subsidies. For instance, the EU’s Green Deal has established a target of 300 GW for 2050 and 60 GW for 2030 in offshore wind capacity [37]. This has underscored the EU’s political commitment to fostering renewable growth [37]. Likewise, the United States intends to install approximately 30 GW of offshore energy by 2030, marking a significant policy shift toward clean energy generation [37].

F. 

Challenges and Future Prospects

Despite its promising outlook, the offshore wind sector faces several significant challenges. Technical obstacles, including grid integration complexities, high capital investment demands, and conflicts with other maritime activities, such as fishing and shipping, present significant hurdles [55]. These challenges require ongoing innovation, stakeholder collaboration, and supportive policy frameworks.

The offshore wind industry is anticipated to grow in the coming years, with projections indicating that the global installed capacity will exceed 228 GW by 2030 [53]. These developments, combined with favorable factors related to scale effects and appropriate policies, are expected to drive this growth, positioning offshore wind energy as a key player in the future of the global renewable resource landscape.

3.7. Current Analytical Practices and Challenges

Performance analysis of offshore wind farms includes various methods for evaluating and optimizing energy production and operational efficiency. Traditional analytical procedures are based on deterministic models that utilize historical data to predict future behavior. These models often depend on statistical methods to analyze wind speed distributions, estimate energy production, and evaluate turbine performance [2]. While these techniques offer valuable insights, they need to be enhanced to better address natural wind resource variability and stochastic elements.

To address these deficiencies, probabilistic models have been explored to include the effect of uncertainties regarding wind speed and wind direction (and other factors). As they stand, these models employ techniques such as Monte Carlo simulations and statistical differential equations, which sample solutions from a distribution of possible solutions to explore how outcomes may behave under various performance contexts [36]. However, because of the model’s complexity, many computational resources and technical expertise are needed, making applying the models to operational contexts challenging.

In recent years, ML algorithms have become valuable tools for characterizing and forecasting the performance of offshore wind farms. ML models, e.g., artificial neural networks (ANNs), support vector machines (SVMs), and ensembles, e.g., random forests, have been proven to perform better than classical statistical models [6]. This algorithm can be applied to modeling non-linear, intricate relationships between input parameters and the performance measure (energy yield) and the systemic behavior generated in terms of the energy yield, thus enabling a better estimate of energy yield and systemic behavior.

Despite their advantages, the implementation of ML models in offshore wind analysis presents several challenges:

-

Data Requirements: High-quality datasets are essential for practical model training. Data paucity, mainly when dealing with young offshore wind markets, has also been demonstrated to restrict the ability to develop robust ML models [31].

-

Model Interpretability: Black-box ML models are often criticized for needing more transparency, making it difficult to understand how they arrive at decisions [20].

Integration Challenges: Many diagnostic tools and software platforms, however, are not suitable for using the latest ML algorithms, which makes them inapplicable to conventional ML algorithms, requiring a radical change in these systems [49].

-

Adaptability to Dynamic Conditions: Due to the nature of dynamic offshore zones, real-time-updating models are required, and many standard and ML models are defective in this ability [28].

In conclusion, despite using traditional analytical techniques to obtain valuable insights into the operation of offshore wind farms, the domain and challenge of non-linear interactions and dynamic environmental factors can be appreciated. However, the analytical capabilities of those models and computational characteristics of probabilistic approaches are only sometimes sufficient to account for the behavior of complex offshore wind systems. This highlights the importance of more sophisticated analytical tools capable of coping with these challenges.

Due to the advancement of ML algorithms, there is a promising answer to these limitations. ML techniques allow the modeling of non-linear relationships and adaptive overtime, which enhances prediction accuracy and operational efficiency. However, achieving the potential of ML analysis of offshore wind energy is constrained by addressing problems related to, among others, the availability of data, model interpretability, system integration, and fluctuations.

Accordingly, ML is described in the next paragraph as an element of renewable energy applications and in a Special Issue from the point of view of ML applications to offshore wind energy systems. It addresses ML techniques to solve the problems outlined in conventional (i.e., ‘static’) analytical practices, including how they have been utilized for forecasting, optimization, and decision-making tasks.

Considering the evolution and status of ML-based applications, this establishes the background for further investigating the applications of these technologies on offshore wind farm operations to enhance their performance and sustainability, i.e., in the context of the researchers’ aims.

3.8. Role of ML in Renewable Energy

i. 

Forecasting and Prediction

ML has been increasingly integrated into renewable energy systems to enhance efficiency, reliability, and scalability. ML algorithms leverage large datasets to identify patterns and predict variables to optimize various renewable energy generation and distribution touchpoints. Accurate energy production and consumption forecasting is significant for renewable energy resources. Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) have been used in the forecasting of both solar irradiance and wind speeds. Better scheduling and, hence, energy assets dispatch is realized. For instance, deep learning algorithms have improved the reliability of short-term wind power forecasting to mitigate uncertainty about integrating wind power into power networks; among these applications are works by Liu et al. [72].

ii. 

Optimization of Energy Systems

ML lies at the very core of performance optimization in sustainable energy systems. The techniques involve using genetic algorithms and reinforcement learning on an optimal setup and operation of the photovoltaic system to maximize the energy yield while minimizing costs. In wind energy, ML models have been created for fine-tuning the positions of turbines and maintenance diagnostics, which enhances overall efficiency with minimal hours of downtime [34].

iii. 

Fault Detection and Maintenance

The installation of fault detection and predictive maintenance using ML further enhances the reliability of renewable energy devices. The ML algorithm can learn from operating data to identify deviations from normal behavior, indicating that a fault is likely to occur. Proactive maintenance can be initiated to prevent unplanned outages [4].

ML algorithms have been trained with data on predicting bearing failures in wind energy systems; this aims to prevent damage to turbine parts and increase service life mechanically.

iv. 

Energy Management and Smart Grids

ML for smart grids in energy management is used for distribution. Accordingly, algorithms have been employed for supply and demand balance, the integration of distributed energy resources, and grid stability [16]. In microgrids, ML models are utilized to grid the storage, generation, and load economics and provide an economically feasible, uninterruptable power supply with renewable resources [6].

v. 

Challenges and Future Directions

Challenges persist in integrating ML into renewable energy systems:

-

Data Quality: The datasets used for training the model should not be of low quality, and any discrepancies within the data will affect the resultant performance [12].

-

Model Complexity in Practice: ML models are inherently complex in their application and have interdisciplinary proficiency.

-

Scalability: What is more important is that scalable ML models can be built from ever-larger datasets and system sizes.

Based on the literature review regarding the role of ML in renewable energy, this development and future direction suggest great potential for enhancement by combining it with conventional energy and exergy analyses regarding ML [40]. Integration considering how ML will allow for a more profound and accurate determination of energy and exergy balances is vital.

3.9. Integration of ML with Energy and Exergy Analysis

I. 

Advances in Energy and Exergy Analysis through ML

Combining ML with the predictive capabilities of thermodynamic assessment allows for identifying inefficiencies and designing strategies to optimize system performance overall [40]. Advancements using ML Background Conventional exergy and energy analyses have been modified with ML algorithms. This combination represents one of the most innovative approaches observed today for the performance and efficiency optimization of diverse energy systems. Such a synergy combines powerful ML capabilities in prediction with traditional thermodynamic analyses, providing in-depth insight into systems’ behavior and arriving at more efficient decision-making.

Energy and exergy analyses are the basis of performance assessment in energy systems. Exergy analysis is more accurate and detailed than a performance description since it considers the quality of energy conversions and the identification of irreversibility in the processes [5]. The inclusion of ML methods in such analyses has resulted in improvements such as

-

Predictive Modeling: For this reason, ML algorithms (e.g., artificial neural networks (ANNs) and support vector machines (SVMs) have been applied to the prediction of overall energy consumption and energy dissipated as exergy loss in multilevel systems. For instance, Ardabili et al. [13] have successfully implemented hybrid neuro-fuzzy architectures to forecast exergy destruction in heating, ventilating, and air conditioning (HVAC) plants with high accuracy and robustness [13].

-

Optimization: ML allows the adaptive optimization of energy systems by discovering operating conditions that minimize exergy losses; Sabzehali et al. also applied the turbofan engine through deep learning models, enhancing energy and exergy efficiencies [17].

-

Fault Detection and Diagnosis: Integrating ML with exergy analysis enhances fault detection capabilities. Based upon insights into information loss and exergy performance degradation, ML models can not only determine an energy system’s inefficiencies but also create a new route for implementing preventive maintenance programs [15].

II. 

Applications for Renewable Energy Systems

The combination of ML with energy and exergy analysis has been beneficial for renewable energy systems:

-

Wind Energy: ML models have been trained to forecast wind turbine performance and control variables, theoretically minimizing exergy loss and overall efficiency [19].

-

Solar Energy: In photovoltaic devices, ML algorithms have been used to estimate solar irradiance and find the best orientation of panels, which results in higher energy capture and lower exergy losses [15].

III. 

Challenges and Future Directions

Although progress has been made, some problems remain when integrating ML into energy and exergy analysis.

-

Data Quality and Availability: Real-world, high-fidelity, realistic data help train realistic ML models. However, undesirable or noisy data can lead to low or unreliable model performance [15].

-

Model Interpretability: The complexity of the ML models sometimes limits their interpretability, making it difficult to gain valuable information from the analysis derived. Model interpretation is still an active research topic [15].

-

Computational Resources: State-of-the-art ML models, especially DL networks, are computationally demanding, which can limit them to real-time applications [13].

Future research should concentrate on overcoming these challenges by creating more effective algorithms, improved methods for data acquisition, and a more transparent model interpretation. Specifically, probing the combination of ML with new emergent technologies (e.g., the Internet of Things (IoT) and digital twins) could significantly improve the effectiveness of energy and exergy analysis.

3.10. Software Tools and Simulation Models

I. 

Wind Farm Design and Optimization Tools

The evolution of highly complex software instruments and modeling systems has dramatically enhanced contributions to the boom in offshore wind power. These tools enable the design, analysis, and optimization of wind farm operations, allowing stakeholders to make informed decisions that improve efficiency and sustainability.

An appropriate wind farm design requires evaluating several factors, such as wake licensing, turbine positioning, and energy prediction. Many of these capacities are available through software tools.

A. 

Wind Farmer:

-

Developer: DNV—Available at: https://store.veracity.com/windfarmer-analyst-license (Accessed: 5 October 2024) [51].

-

Key Features: Wind Farmer is a general-purpose computer package for wind farm design and analysis. It uses DNV’s broad experience in the global assessment of wind farm energy production and global operational data for the accurate power production of future wind farm energy. Wind Farmer allows users to design wind farms, make highly accurate annual energy estimates, and perform full wake and blocking simulations.

-

Applications: Widely used for designing wind farms, performing energy assessments, and modeling wake and blockage effects.

-

Limitations: Requires a learning curve for new users to utilize its advanced features fully.

B. 

Open Wind Power:

-

Developer: Bentley Systems—Available at: https://www.bentley.com/software/openwindpower/ (Accessed: 10 October 2024) [48].

-

Key Features: Wind Farmer is a general-purpose computer package for wind farm design and analysis. It uses DNV’s broad experience in the global assessment of wind farm energy production and global operational data for the accurate power production of future wind farm energy. Wind Farmer allows users to design wind farms, make highly accurate annual energy estimates, and perform full wake and blocking simulations. OpenWindPower is a specialized suite for offshore wind turbine and substructure modeling. The software performs advanced finite element analysis to study wind turbines and their support structures including foundations, towers and floating platforms. The software performs dynamic simulations under wind, wave and current loading conditions.

-

Applications: Effective in an offshore structure’s wind tower/pile system design, including fatigue and load analysis.

-

Disadvantages: Possibly not overly user-friendly and requires configuration for integration with other utilities.

C. 

OpenFAST:

-

Developer: National Renewable Energy Laboratory (NREL) OpenFAST—Available at: https://openfast.readthedocs.io (Accessed: 15 October 2024) [57].

-

Key Features: OpenFAST is an open access wind turbine simulation framework based on the FAST v8 code and extended to simulate fixed-bottom, land-based, and even floating offshore wind turbines. OpenFAST provides a multi-physics-based engineering environment for wind farm analysis.

-

Applications: Simulation of dynamic response in different atmospheres on wind turbines, etc.

-

Drawbacks: Steep learning curve; using simulation environments requires prior knowledge.

D. 

Openwind:

-

Developer: UL Solutions—Available at: https://www.ul.com/services/openwind (Accessed: 8 October 2024) [81].

-

Key Features: Openwind is wind farm design optimization software used throughout a wind project’s life cycle. It generates optimum turbine designs to achieve the highest energy yield with the minimum energy losses and considers the cost of plant growth, hence achieving overall project efficiency.

-

Applications: Suitable for optimizing wind farm layout to maximize energy output and efficiency.

-

Limitations: Sometimes expertise is required to leverage optimization features fully.

E. 

WAsP—Wind Atlas Analysis and Application Program

-

Developer: DTU Wind Energy (WASP)—Available at: https://www.wasp.dk (Accessed: 17 October 2024) [82].

-

Key Features: WAsP is a professional software tool for all aspects of wind resource assessment, siting, and energy yield calculation for wind turbines and wind farms. It covers models and tools at every step in the process, from analyzing wind data to calculating energy yield from an entire wind farm.

-

Applications: Widely used in site analysis and wind farm planning, especially in those areas with simple terrain.

-

Limitations: Its accuracy is reduced in complex terrains due to linear flow model deficiencies.

F. 

Windographer:

-

Developer: UL Solutions, Windographer|Wind Data Analytics and Visualization Solution, UL Solutions, 2024—Available at: https://www.ul.com/software/windographer-wind-data-analytics-and-visualization-solution (Accessed: 2 November 2024) [59].

-

Key Features: Windographer is designed to import, analyze, and visualize wind resource data from met towers, solar, or lidar. It offers functionalities for rapid data intake, quality control, statistical processing, and results output in various popular flow models.

-

Applications: Plays an essential role in assessing wind resources and validating data.

-

Limitations: It may need to be more fully integrated with advanced modeling tools.

G. 

WindPRO:

-

Developer: EMD International A/S developed WindPRO, considered the best software for designing wind farms and PV projects—Available at: https://www.emd-international.com/software/windpro (Accessed: 2 November 2024) [60].

-

Key Features: WindPRO is a full-featured software package for wind farm design and planning, including all aspects from wind data analysis and energy yield calculation to environmental impact assessment and site suitability assessment.

-

Applications: Extensively used in wind project development, feasibility studies, and detailed project planning.

-

Drawbacks: The high licensing costs associated with the software’s rich functionality can be considered in smaller projects.

H. 

WindSim:

-

Developer: WindSim AS Software—Available at: https://www.windsim.com (Accessed: 2 November 2024) [83].

-

Key Features: WindSim utilizes CFD to model wind flow in complex terrains, allowing 3D visualization and detailed wind resource assessment.

-

Applications: Perfectly suited for wind resource assessment over complex terrain, enables accurate site and turbine placement.

-

Drawbacks: The software requires computer resources, processing power, and the know-how to strengthen it.

The development of open-source modular platforms such as the Python-based WindML toolkit represents a critical solution to address interoperability gaps. These tools would allow seamless integration with current software through API connections, which support plug-and-play ML workflows. Future platforms for floating offshore wind turbines (FOWTs) need to include fully coupled platform–mooring–environment dynamics, as shown in seismic response studies [45,46,47].

3.11. Simulation Models for Offshore Wind Applications

These simulation models are central to understanding the complex interrelations between components of offshore wind farms, including aerodynamics, hydrodynamics, and structural dynamics.

SOWFA is a collection of CFD solvers, boundary conditions, and turbine models coded in the OpenFOAM CFD toolbox developed at NREL. It provides a vehicle for studying wind turbine and wind plant performance and loads for all possible atmospheric conditions and terrain [57].

-

Farmwise: Farmwise, developed by Sener, is a digital modular system that enables the design of offshore wind farms based on environmental factors. Its algorithms can lead to turbine layout decisions regarding technical and economic feasibility by considering aspects like wind direction, seabed topography, exclusion, attachment, and cable-laying [62].

-

Shoreline Wind: Created by Shoreline, it provides a whole-lifecycle wind farm solution set covering design, construction, and operations and maintenance (O&M). It is a design solution to allow simulation, modeling, and analysis of a complete wind farm in a risk-free virtual space, enabling effective decision-making and optimization [58].

Table 4. Simulation models for offshore wind applications.

Simulation Model	Developer	Key Features	Applications	Limitations
SOWFA (Simulator for Wind Farm Applications)	National Renewable Energy Laboratory (NREL), Golden, CO, USA. Available at: https://github.com/NREL/SOWFA (Accessed: 15 October 2024)	Set of computational fluid dynamics (CFD) solvers, boundary conditions, and turbine models based on the OpenFOAM CFD toolbox; allows investigation of wind turbine and wind plant performance under various atmospheric conditions and terrains	Wind turbine and wind plant performance analysis; loading assessments; atmospheric condition simulations	Requires expertise in CFD and familiarity with OpenFOAM; computationally intensive
Farmwise	Sener, Getxo, Biscay, Spain. Available at: https://www.group.sener/wp-content/uploads/proyectos/farmwise-advanced-simulation-of-offshore-wind-farms.pdf (Accessed: 4 April 2025)	A digital and modular tool that optimizes offshore wind farm design based on environmental variables; algorithms enable decision-making for technically and economically efficient turbine layouts, considering factors such as wind direction, seabed conditions, exclusion zones, attachment costs, and cable laying	Offshore wind farm design optimization; turbine layout planning; cost and production evaluation	May require customization for specific project needs; dependent on accurate environmental data
Shoreline Wind	Shoreline AS, Stavanger, Norway. Available at: https://shorelinewind.com (Accessed: 10 October 2024)	The suite of solutions for the entire lifecycle of wind farms, including design, construction, and operations and maintenance (O&M); the design solution allows for simulation, modeling, and analysis of entire wind farms in a virtual environment, facilitating efficient decision-making and optimization	Wind farm lifecycle management; risk-free virtual simulations; decision-making support; optimization of design, construction, and O&M processes	It may require integration with other tools for comprehensive analysis; effectiveness depends on user proficiency and data quality.

provides a comprehensive overview of the simulation models for offshore wind applications.

3.12. Integration of ML in Simulation Tools

The integration of ML algorithms into simulation tools has enhanced the predictive capabilities and optimization processes in offshore wind energy systems:

-

ExaWind: An open-source suite of codes designed for the multi-fidelity simulation of wind turbines and wind farms, ExaWind includes high-fidelity simulations that resolve scales from micro-scale boundary layers around turbine blades to kilometer-scale turbulent atmospheric flow. It comprises three physics-based codes, AMR-Win, Nalu-Wind, and OpenFAST, providing a comprehensive environment for testing ideas, including potentially disruptive technology, before development [52].

-

FLORIS (FLOw Redirection and Induction in Steady State): Developed by NREL, FLORIS provides a computationally inexpensive, controls-oriented modeling tool of the steady-state wake characteristics in a wind farm. This open-source software framework models turbine interactions in planned and existing wind power plants and can be used to design and analyze wind farm control strategies and layout optimizations [63].

Table 5. Integration of ML in offshore wind simulation tools.

Tool	Developer	Key Features	Applications	Limitations
ExaWind	Exascale Computing Project (ECP), Oak Ridge, TN, USA. Available at: https://github.com/exawind (Accessed: 20 October 2024)	Open-source suite for multi-fidelity simulation of wind turbines and farms; includes high-fidelity simulations resolving scales from micro-scale boundary layers to kilometer-scale atmospheric flow; comprises AMR-Wind, Nalu-Wind, and OpenFAST codes	Testing innovative wind energy concepts, simulating complex interactions within wind farms, and evaluating potential disruptive technologies before development	High computational requirements; steep learning curve for new users
FLORIS (FLOw Redirection and Induction in Steady State)	National Renewable Energy Laboratory (NREL), Golden, CO, USA. Available at: https://github.com/NREL/floris (Accessed: 10 October 2024)	Open source, controls-oriented modeling tool for steady-state wake characteristics in wind farms; computationally efficient; models turbine interactions in planned and existing wind power plants; aids in designing and analyzing wind farm control strategies and layout optimizations	Wind farm control strategy development, layout optimization, and performance analysis of turbine interactions	May not capture transient effects due to steady-state assumptions; requires validation for specific site conditions

shows a comprehensive overview of the integration of ML in offshore wind simulation tools.

3.13. Practical Applications and Use Case Scenarios of Offshore Wind Software Tools

The practical application of software tools and simulation models in offshore wind energy analysis becomes more relevant when Section 3.9, Section 3.10, Section 3.11 and Section 3.12 demonstrate their use through experimental applications or specific use-case scenarios. The following examples illustrate how OpenFAST, FLORIS, and WindFarmer tools can be used in realistic operational conditions to solve real-world problems. These scenarios demonstrate their practical capabilities in design validation, energy yield prediction, the optimization of turbine positioning, wake management, and other critical aspects of offshore wind farm management.

A. 

OpenFAST (Aero-Servo-Elastic Simulator)

OpenFAST represents an open-source simulation tool that combines aero-hydro-servo-elastic modeling for wind turbines to analyze aerodynamics and structural dynamics, hydrodynamics, and control systems. The simulation tool is the primary choice for offshore turbine time-domain modeling at detailed levels. The research by Branlard et al. (2024) used OpenFAST to model the full-scale floating turbine TetraSpar prototype with its moorings, hydrodynamics, elasticity, and NREL ROSCO controller, and performed SCADA model calibration [64]. Frederik et al. (2025) [65] used OpenFAST to connect with high-fidelity large-eddy simulations (LES) of the offshore boundary layer for studying a two-turbine farm under realistic wind shear and veer conditions. The research used precursor LES to create inflow matching lidar-based offshore data, which was then used to evaluate wake-steering control through OpenFAST turbine models [65]. OpenFAST demonstrates its capability to model turbine loads and performance through simulations of wind/wave conditions and control actions such as wake steering and pitch control in offshore environments.

The OpenFAST framework enables wind farm analysis through multi-turbine simulations using extensions such as FAST-Farm or flow solver coupling. Researchers have used OpenFAST to model realistic farm layouts containing dozens of turbines to study energy production and structural loads under storm and turbulent inflow conditions. Project teams employ OpenFAST to evaluate new turbine designs and control strategies by analyzing how different blade pitch schedules affect load reductions during extreme gusts [64,65]. OpenFAST delivers precise dynamic responses, which enable design verification and support the development of digital twins for offshore turbines.

B. 

FLORIS (Wind Farm Flow Model)

The open-source engineering model FLORIS (FLOw Redirection and Induction in Steady State) serves for wind-farm flow and control analysis. The model determines wind farm power output by analyzing inflow conditions together with turbine layouts that include wake interactions and yaw misalignment effects (wake steering) [66]. FLORIS provides practical applications to measure energy production and control benefits across different operational conditions. Simley et al. (2024) [66] used FLORIS to model the Horse Creek offshore wind farm during westerly flow at 8 m/s and 8.75% turbulence intensity. The authors used FLORIS to determine the flow field and turbine power output during baseline operations and optimal wake-steering yaw strategy implementation, which revealed better downwind speeds and reduced wake deficits [66]. The model creates visual “flow maps” and calculates annual energy production (AEP) for different control schemes on existing farm layouts.

FLORIS is also used for farm layout and control optimization studies. For example, after calibration to offshore wind data, Doekemeijer et al. (2022) and colleagues used FLORIS to optimize turbine positions and yaw angles within realistic lease areas, maximizing AEP under constraints like fixed grid spacing [67]. Some other researches also confirm these findings [84,85,86]. Figure 7 shows the northing calibration for Anholt Turbine 32, which is essential for accurately modeling the wind farm layout in offshore wind simulations using FLORIS [67].

FLORIS operates through time-series or wind rose scenarios, which present different wind speed and direction conditions with turbulence and shear/veer characteristics to determine yield for each layout candidate. The tool features a modular wake model, which enables users to evaluate multiple wake-loss models through its recent versions of the Gaussian-curl hybrid model. Engineers apply FLORIS to measure the yearly power benefits of wake-steering controls and optimized layouts based on specific site conditions, including wind distribution, TI, and shear values [66]. The project team demonstrates wake steering benefits through the FLORIS input of site wind roses, which reveals that the technique generates approximately 5% additional energy during low-turbulence conditions [66].

C. 

WindFarmer (Energy Yield and Farm Design)

DNV developed WindFarmer as a commercial wind farm design and energy assessment tool. The tool serves the entire wind industry for conducting pre-construction energy yield studies and layout optimization. WindFarmer implements multiple recognized wake-loss and blockage models, including Park and Eddy-Viscosity and Large-WS-Blockage, for the sensitivity analysis of modeling assumptions. WindFarmer Analyst serves BlueFloat Energy as an offshore wind developer tool to determine floating offshore farm wake and blockage losses while optimizing turbine positions. The company uses WindFarmer to run multiple wake models, which decreases uncertainty before establishing a reliable project forecast [68]. The software combines blockage effect analysis with multiple wake formulas to let engineers evaluate baseline AEP against scenarios that include array-wide axial induction control and yaw-offset steering.

WindFarmer operates as a standard tool for real project applications because DNV reports its use in developing approximately 340 GW of wind projects worldwide, including onshore and offshore facilities [69]. The typical workflow for an analyst involves entering site metadata together with candidate turbine types into WindFarmer, followed by the computation of monthly and annual energy production for each design scenario using the tool’s wake models. The analyst would evaluate two offshore lease layout options by running WindFarmer simulations to determine the AEP for each design, then calculating the AEP difference. The tool enables users to make flexible comparisons through its model switching feature and blockage inclusion capability. WindFarmer provides industry users with financial performance confidence for wind farm designs through its validated methodology and advanced modeling capabilities [68,69].

D. 

Other Tools (WindSim, ExaWind, etc.)

Offshore wind analysis employs multiple tools beyond the ones mentioned earlier. WindSim and other CFD-based models perform three-dimensional flow and wake modeling for complex terrain and wind farm layouts by solving the Reynolds-averaged Navier–Stokes equations to predict wind fields and turbine interactions in challenging sites. The WindSim simulations use a computational domain that includes z, representing elevation above sea level in meters, and $z_s$, representing surface elevation (Figure 8) [70].

The development of exascale computing tools enables researchers to extend their capabilities in whole-farm simulation. The ExaWind suite from the U.S. Department of Energy functions as an open source HPC-based environment that enables the multifidelity simulation of complete wind plants. The ExaWind platform unites three physics-based codes, Nalu-Wind and AMR-Wind (CFD solvers for unstructured and structured grids) and OpenFAST (turbine aero-servo-elastic modeling), to create a unified platform for wind power plant modeling [71]. The ExaWind Nalu-Wind simulation in Figure 9 shows the flow structure surrounding an NREL 5 MW wind turbine rotor (developed by the National Renewable Energy Laboratory, Golden, CO, USA) [71].

The ExaWind simulation in Figure 10 demonstrates turbulent wind flow over ocean waves through a moving-wave boundary condition that represents wind–wave interactions. The figure demonstrates ExaWind’s ability to integrate atmospheric turbulence with wave dynamics, which is essential for floating offshore wind turbine design and analysis [71].

3.14. Challenges and Future Directions

While significant progress has been made in developing software tools and simulation models for offshore wind energy, several challenges persist:

-

Data Quality and Availability: High-quality, comprehensive datasets are essential for accurate simulations and model training. Inferring from noisy or insufficient data can decrease model performance and reliability.

-

Model Complexity and Computational Resources: State-of-the-art simulation models, especially ones that use ML algorithms, are computationally heavy, and the limitations on the available computing capacity may hamper their use in real-time applications.

Integration and Interoperability: Standardization of protocols and data formats still needs to be solved, as it concerns the seamless integration and interoperability of various software tools and models.

ML models deployed on edge devices such as FPGAs in wind farms would eliminate cloud dependency while providing real-time optimization capabilities. Edge computing would decrease fault detection and adaptive control latency, especially in the dynamic offshore environment. Based on the progress and problems previously recognized in the design and use of software tools and simulation models, a solid methodological framework is also needed to use ML algorithms effectively for offshore wind energy systems. The subsequent section delineates the methodological approach adopted in this research, focusing on the systematic data collection and preprocessing processes, model development and validation, and software integration and deployment.

The outlined methodology aims to address the identified challenges (e.g., algorithm efficiency, data quality, model interoperability) and exploit emerging technologies (e.g., the Internet of Things (IoT) and digital twins). This framework is critical to developing an integrated machine-learning-based software solution for operationalizing offshore wind farms, increasing predictive power, and enabling more sustainable and efficient renewables.

3.15. Methodological Approach

The proposed methodological approach builds on the software tools and simulation models from the previous subsection to address their limitations through machine learning algorithms for improved predictive accuracy and real-time adaptability. The proposed framework utilizes the multi-physics simulation and wake modeling capabilities of OpenFAST and FLORIS tools to address data variability and limited real-time processing and model interpretability through advanced machine learning techniques. The integration allows for creating a software solution that both simulates complex offshore dynamics and optimizes operational parameters across different environmental conditions.

A new conceptual diagram has been introduced to illustrate the integration of ML algorithms, digital twins, IoT sensor networks, and advanced simulation models. This diagram visually underscores the interconnectivity between data acquisition, real-time processing, predictive analytics, and adaptive control—a blueprint for future offshore wind farm management software development. ML algorithms analyze and process real-time data from IoT sensor networks, with advanced simulation models and digital twins improving predictive analytics. This integrated framework facilitates adaptive control and optimizes offshore wind farm operations (see Figure 11) [76]. Future digital twins need multiphysics coupling (e.g., fluid–structure–soil interactions) through tools like ANSYS-2024 R2 and ExaWind to enhance realism. Validating ML models under these conditions would improve robustness for floating wind turbines in harsh marine settings, particularly for fully coupled platform–mooring–environment dynamics, as demonstrated in seismic response studies [45].

In this respect, designing an integrated, ML-based software solution for the optimal control of operations within an offshore wind farm requires a systematic and methodical framework encompassing all the flow phases, from preprocessing to the training phase to postprocessing. This covers data acquisition and preparation, model construction and evaluation, and software deployment. Each phase should contribute to the tool’s reliability, precision, and applicability.

i. 

Data Gathering and Pre-processing

-

Data collection and preprocessing are the very heart of all ML applications. In the case of offshore wind power plants, such data can relate to the following.

-

Meteorological Data: Wind speed, direction, temperature, and humidity.

-

Data: Turbine performance metrics, power output, maintenance records.

-

Environmental Data: Sea state conditions, wave heights, tides.

Correct data quality is of paramount importance, as the presence of inconsistencies or errors in data can negatively influence the performance of the models. Data cleaning, normalization, and feature extraction are applied to preprocess data for modeling. Recent works have highlighted the capabilities of thorough data preprocessing to improve the accuracies of ML models used in wind energy applications [9,21].

ii. 

Model Development and Validation

The construction of predictive models is the process of identifying the correct ML algorithms and training such algorithms with pre-processed data. Commonly used algorithms in this context include

-

Multiple Linear Regression (MLR): Provides a natural handle on the semantics of linear dependence among variables.

-

Random Forest (RF): It offers a high level of robustness to overtraining and non-linear and complex interactions.

-

Long Short-Term Memory (LSTM) Networks: As it is vital in temporal dependency extraction, it can be, for instance, useful for time-series data (and are always present for wind energy systems).

Model validation is carried out by cross-validation and Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. Recently, it has been demonstrated that such models can also be employed to forecast the behavior of wind farms [18,30].

iii. 

Software Integration and Deployment

The practical application also requires implementing these developed models into a helpful software platform. This involves

-

Designing an Intuitive Interface: Offers operators a way to make data inputs, present results, and obtain helpful information.

-

Real-Time Data Processing: This enables data ingestion and processing into the real-time live data stream, enabling on-the-spot decision-making.

-

Operational Recommendations: Recommendations for operational modifications to improve energy and exergy efficiencies are also presented.

-

Scalability and Adaptability: The software can be applied to test various offshore wind farm designs and sizes.

Studies have pointed out that such integrative networks contribute to the decision-making process for renewable energy systems [29,54].

iv. 

Challenges and Considerations

Herein are some of the limitations that may be encountered in the software tool’s design and development:

-

Data Variability: Offshore is, by definition, a highly dynamic setting, so measured data can be equally trustworthy (and variable). Strong preprocessing and adaptive models can solve all these problems [22].

-

Model Interpretability: Since they are complicated, advanced ML models and intense learning models (e.g., LSTM) are complex and sometimes impossible to interpret. Embedding explainable AI approaches can make the model view more understandable and believable [54].

-

Integration with Existing Systems: Successful penetration highly depends on the ability to integrate with the current operation system and workflow. Modularizable, interposable SW architectures have served as a valuable means of addressing embodiment [14].

Following a detailed characterization of the completeness methodology needed to connect ML with the dynamics of offshore wind farms, it is relevant to discuss what is learnable and what can be learned from this cycle.

Based on surveying the developments, current limitations, and future directions reached in this interdisciplinary research, an understanding can now be gained of the direction of ML and its implications in the optimal operation of offshore wind energy systems. This review article will not only discuss the performance of the proposed developments but also the open research points and problems that will be highlighted to serve as a platform for future research and development, and ultimately to achieve the goal of efficient and sustainable offshore wind farm operations.

4. Discussion

The first classification of References by Category highlights the broad spectrum of ML applications to offshore wind energy systems. The classification system both structures existing research and demonstrates the increasing dependence on ML solutions for offshore wind farm operations, which are essential for reaching worldwide renewable energy targets. The emphasis on energy and exergy optimization, predictive maintenance, and performance enhancement is symptomatic of the industry’s focus on efficiency, reliability, and sustainability improvement efforts. By using ML techniques, researchers and practitioners can address the issues arising during the operation of offshore wind farms to a greater extent of complexity, thus leading to their designation as renewable sources of energy technologies.

On the other hand, ML and energy and exergy analysis represent a fundamental advancement towards optimal energy system design. The studies reviewed demonstrate how ML integration with energy and exergy analysis improves system performance optimization through the identification of inefficiencies. ML’s predictive and analytical capabilities allow scientists and practitioners to make more accurate assessments, optimize performance, and implement preventive maintenance measures. By overcoming the current challenges’ mitigation, the future impact of this integration will be significantly enhanced and will subsequently help shape more efficient and robust energy systems.

Once it is clear how vital ML is to improving energy and exergy analyses, it will be realized that the practical realization of these new approaches depends upon the existence of powerful software packages and simulation models between theoretical ML models and their practical deployment in conditions of the natural world, such as simulating large offshore wind energy systems, examining big data, and optimizing performance parameters. This subsection discusses the status of the software packages and simulation works on offshore wind energy and how far the tools have engaged ML algorithms in their design, operation, and maintenance processes. For example, tools like OpenFAST and FLORIS have begun integrating ML to simulate complex offshore dynamics, enhancing predictive accuracy. This investigation is of great importance for understanding how integrated software solutions can be used to enable the use of ML methods and, in turn, to improve the efficiency and environmental footprint of offshore wind farms.

In addition, integrating ML with energy and exergy analysis for offshore wind farms represents a significant advancement in addressing these operational and environmental challenges. Specifically, ML addresses challenges such as unpredictable weather patterns, turbine degradation, and real-time decision-making in remote offshore locations. Offshore wind energy is increasingly recognized as a critical resource in the global transition toward renewable energy, providing substantial benefits such as higher energy yields, reduced land use, and less visual impact than onshore installations. However, the harsh and dynamic marine environment complicates operations, maintenance, and optimization. The reviewed literature highlights how ML can be transformative in overcoming these challenges.

One of the main applications of ML in offshore wind systems is optimizing energy and exergy performance. Studies by Masoumi and Forootan et al. show how ML algorithms can analyze large datasets and identify inefficiencies in real time [9,15]. ML algorithms, especially neural networks and hybrid models, use meteorological, operational, and environmental data to assess energy loss accurately. They enable real-time identification of inefficiencies, allowing immediate corrective actions, thus reducing exergy destruction and ecological impacts and increasing overall energy yields.

The systematic review demonstrates that ML methodologies substantially improve prediction accuracy. The ML-enabled methods demonstrated notable accuracy improvements, which reached 92% for energy analysis and 80% for exergy analysis, compared to traditional methods that achieved 75% and 65% accuracy, respectively (as shown in Figure 12) [6,9]. The results demonstrate that ML applications excel in offshore wind farm analysis by showing their practical advantage over traditional methods [6].

The substantial improvement in accuracy enables operational decisions for proactive maintenance, real-time energy optimization, and fault prediction, which aligns with global renewable energy goals. The empirical findings provide strong evidence to support the implementation of ML-based methods for improving the operational efficiency and sustainability of offshore wind farms.

Other critical applications are predictive maintenance and fault detection. Offshore wind turbines experience significant wear and tear due to their exposure to harsh marine conditions, which can result in unexpected failures and expensive downtime. ML models, such as support vector machines and clustering techniques, have been shown to predict turbine failures well in advance, facilitating proactive maintenance strategies. Turnbull et al. showed that support vector machines could predict bearing failures with an accuracy of 85% and reduce maintenance costs by 20% [4]. Turnbull et al. and Lützen and Beji stress that these techniques increase the lifespan of turbines and improve system reliability by identifying deviations in operational patterns [4,29]. This capability is vital for offshore environments, where maintenance logistics are more complex and costly than their onshore counterparts.

The environmental aspect of offshore wind energy also benefits from ML applications. Studies by Bailey et al. and Copping et al. highlight the use of ML in assessing potential ecological risks and developing mitigation strategies [27,35]. The application of ML is significant for ensuring that offshore wind farms contribute positively to marine ecosystems while minimizing their environmental footprint. ML models can simulate environmental impacts, which can help select optimal locations for wind farms and ensure that sustainability goals are met. In addition, offshore turbine foundations often act as artificial reefs, promoting marine biodiversity. Using ML, stakeholders can monitor and enhance these ecological benefits while reducing adverse effects on marine ecosystems.

Despite these advancements, the discussion uncovers several challenges that must be addressed to fully realize the potential of ML in offshore wind systems. Data quality remains a significant concern, as high-quality, real-time data are essential for training reliable ML models. The offshore environment, with its remote and dynamic nature, often leads to data inconsistencies due to sensor malfunctions or communication delays. These models are severely affected by inconsistent or noisy datasets, affecting their performance and dependability [56].

Moreover, the interpretability of complex ML algorithms and intense learning models presents a hurdle to their widespread use. Many stakeholders are reluctant to depend on “black-box” models without clearly understanding how decisions are made, as Tao et al. emphasized [20]. This lack of transparency can hinder the adoption of ML solutions, particularly in industries where accountability and risk management are critical.

Another significant challenge is in integrating ML algorithms into existing wind farm management systems. Current operational frameworks often lack the modularity to incorporate advanced predictive models without disrupting workflows. Developing modular, open-source platforms, such as the proposed WindML toolkit, could facilitate the seamless integration of ML models into existing systems. Studies by Fischetti, Fraccaro, and Ambarita et al. highlight the importance of developing adaptable software architectures that support incremental ML integration [14,75]. Moreover, offshore wind environments are inherently dynamic, requiring ML models that can adjust to real-time fluctuations in weather, equipment performance, and environmental conditions.

The future of ML in offshore wind systems hinges on addressing these challenges through interdisciplinary collaboration and technological innovation. Multidisciplinary cooperation between data scientists, engineers, and environmental scientists will be essential to develop technically robust and operationally feasible solutions. Improved data-sharing protocols and standardized data formats can enhance data availability and quality, facilitating better model training and validation. Explainable AI techniques can make ML models more transparent and trustworthy, increasing stakeholder acceptance. Furthermore, integrating ML with emerging technologies like the Internet of Things (IoT) and digital twins holds excellent promise. IoT sensors can provide continuous data streams for real-time analysis. At the same time, digital twin platforms allow for the simulation and optimization of wind farm operations in a risk-free virtual environment [40].

Considering the socio-economic and regulatory dimensions is crucial in light of these technological advances. Recent policy frameworks are starting to acknowledge the transformative potential of digital innovations in renewable energy, thus promoting interdisciplinary collaborations and public–private partnerships [23]. By aligning technological advancements with regulatory and socio-economic goals, the offshore wind industry can ensure that ML-driven innovations contribute to environmental sustainability and economic growth. This comprehensive approach is essential for ensuring that emerging technologies align with sustainable practices and the viability of market practices.

Future Directions and Recommendations

To further advance the integration of ML in offshore wind energy systems, several key areas require attention.

Addressing Specific Challenges in Floating Offshore Wind Turbines: The fully connected platform, mooring, and environment system of Floating Offshore Wind Turbines (FOWTs) creates distinctive operational challenges. FOWTs require special design features to handle seismic activities because these events can severely affect their structural performance and operational integrity. The seismic response of floating structures has been investigated through recent studies [45,46,47]. Analyzing and predicting FOWT behavior under seismic loads through machine learning methods will optimize design parameters and create more resilient systems.

Leveraging Edge Computing for Real-Time Optimization: Advanced ML models create computational challenges for real-time applications in offshore wind farms. Edge computing provides a solution through local data processing on Field-Programmable Gate Arrays (FPGAs), which are installed directly within the wind farm. The local processing approach decreases dependence on cloud systems while providing reduced latency and enabling quicker control strategies. Real-time turbine setting adjustments through edge-based ML models would optimize energy capture while reducing component wear using current environmental conditions.

Enhancing Digital Twins with Multiphysics Coupling: Digital twins in offshore wind farm management require multiphysics simulations that include fluid–structure–soil interactions to achieve their full potential. A complete digital twin emerges through the combination of ANSYS for structural analysis and ExaWind for aerodynamic simulations. The integrated model would validate ML predictions under realistic and dynamic marine conditions to ensure robustness and reliability. The multiphysics approach enables researchers to study complex phenomena, including wave load impacts on turbine foundations, which leads to better performance forecasts.

Ensuring Data Security and Traceability with Blockchain: Offshore wind farms require data security and traceability measures to protect sensitive maintenance records and environmental monitoring data. Blockchain technology presents an effective solution through its decentralized tamper-proof ledger system for data transaction recording and verification. Blockchain implementation provides data integrity and confidentiality features that support GDPR compliance. Blockchain technology improves supply chain and maintenance process transparency, building stakeholder trust and accountability.

Validating Hybrid Models in Industrial Settings: The theoretical advantages of hybrid ML models are well established, but their practical effectiveness needs to be proven through real-world implementation. The Dogger Bank project and other operational wind farms can demonstrate how hybrid models (e.g., LSTM with Random Forest) perform in actual conditions. Quantifying benefits such as 15% O&M cost reduction and 10% power generation increase would provide stakeholders with tangible evidence to support the adoption of these advanced techniques.

Developing an Integrated Environmental–Economic Assessment Framework: The long-term sustainability of offshore wind energy projects requires a holistic approach that combines environmental and economic factors. A lifecycle assessment framework enabled by machine learning would evaluate trade-offs using ML models to forecast environmental effects (carbon emissions and ecological disruptions) and financial performance indicators (capital expenditure and levelized energy cost). The tool would help decision-makers create wind farm designs and operational plans that achieve sustainability goals while maintaining financial stability.

In conclusion, applying ML to energy and exergy analysis in offshore wind systems represents a transformative approach to improving efficiency, sustainability, and reliability. The review recognizes essential limitations that need to be addressed to achieve the full potential of ML in offshore wind applications. The lack of high-quality real-time data presents a significant challenge because poor or noisy datasets create performance and dependability issues for models. Standardized data collection methods and enhanced data protocols should be implemented to address these challenges because they ensure reliable and accurate ML-driven predictive models. The review has limitations because various studies employ different methods without standard evaluation tools for ML applications in offshore wind farms. The research exclusion of non-English sources introduced a language-based bias into the study findings. Future research must create standardized evaluation metrics and broader linguistic sources to enhance the evidence base. The review excluded the use of GRADE-style certainty assessment to evaluate the evidence. The narrative synthesis method and wide range of study designs, including simulations and case studies, made it impossible to use the GRADE criteria suitable for clinical trials and comparative outcomes research. Our confidence in the findings emerged from evaluating the methodological strengths against weaknesses in the included studies (as shown in the Risk of Bias Results). The evidence base consists mainly of qualitative emergent findings instead of high-certainty quantitative estimates, which led to the development of these conclusions. The offshore wind industry can maximize ML’s global renewable energy contribution by methodically resolving these limitations using advanced technologies.

5. Conclusions

The systematic review shows that ML applications bring significant advantages to offshore wind farm operations through their applications in energy forecasting and predictive maintenance, and environmental impact assessment. The ML-based methods achieved better predictive accuracy at 92% for energy and 80% for exergy analyses than traditional methods, which reached approximately 75% and 65%, respectively. The enhanced results demonstrate direct evidence for implementing proactive maintenance, exact energy forecasting, and improved operational efficiency. The advancements support the worldwide transition to renewable energy systems by enabling sustainable, low-carbon solutions, confirming ML’s essential position in the global renewable energy transformation.

Implementing AI in healthcare requires addressing data quality, model interpretability, and system integration challenges to achieve full benefits. First, data quality and availability are critical challenges, as accurate predictions rely heavily on high-quality, real-time data. Additionally, the study is constrained by the applicability of ML-based software to specific offshore environments, limiting generalization across varying conditions. Finally, the computational intensity of advanced ML models restricts their deployment in real-time applications, underscoring the need for accessible and efficient solutions.

Future research should resolve the identified limitations by creating algorithms that handle various environmental conditions while improving data quality and real-time functionality. The integration of digital twins with IoT and blockchain technology is highly recommended because these advancements will significantly enhance data security, model transparency, and real-time decision-making capabilities. Implementing these technologies will improve the practicality and scalability of ML methods across various offshore operational settings.

Furthermore, integrating emerging technologies like blockchain for secure data management and advanced analytics for anomaly detection presents an encouraging path for future research. These innovations and enhanced ML models could further optimize operational strategies and diminish the environmental impact of offshore wind farms. The research results provide substantial evidence to both academic and industrial discussions about how ML analytics improves offshore wind farm operations through enhanced efficiency, reliability, and sustainability. The offshore wind industry can strengthen its position in the global renewable energy transition by using current challenges and new technologies. The effective adoption of advanced technologies needs urgent acceleration to fulfill future energy requirements and reduce environmental effects. The accelerating global energy transition requires ML-driven solutions to develop intelligent, sustainable offshore wind farm management systems.