A Dynamic Hidden Markov Model with Real-Time Updates for Multi-Risk Meteorological Forecasting in Offshore Wind Power

Abstract

Offshore wind farms play a pivotal role in the global transition to clean energy but remain susceptible to diverse meteorological hazards—ranging from highly variable wind speeds and temperature anomalies to severe oceanic disturbances—that can jeopardize both turbine safety and overall power output. Although Hidden Markov Models (HMMs) have a longstanding track record in operational forecasting, this study leverages and extends their capabilities by introducing a dynamic HMM framework tailored specifically for multi-risk offshore wind applications. Building upon historical datasets and expert assessments, the proposed model begins with initial transition and observation probabilities and then refines them adaptively through periodic or event-triggered recalibrations (e.g., Baum–Welch), thus capturing evolving weather patterns in near-real-time. Compared to static Markov chains, naive Bayes classifiers, and RNN (LSTM) baselines, our approach demonstrates notable accuracy gains, with improvements of up to 10% in severe weather conditions across three industrial-scale wind farms. Additionally, the model’s minutes-level computational overhead for parameter updates and state decoding proves feasible for real-time deployment, thereby supporting proactive scheduling and maintenance decisions. While this work focuses on the core dynamic HMM method, future expansions may incorporate hierarchical structures, Bayesian uncertainty quantification, and GAN-based synthetic data to further enhance robustness under high-dimensional measurements and rare, long-tail meteorological events. In sum, the multi-risk forecasting methodology presented here—though built on an established HMM concept—offers a practical, adaptive solution that significantly bolsters safety margins and operational reliability in offshore wind power systems.

1. Introduction

Offshore wind power has rapidly gained prominence in recent years, propelled by continuous technological advances and a global movement toward cleaner energy solutions. These installations offer high-capacity energy generation compared to many onshore sites, yet they remain susceptible to a range of complex meteorological conditions—from volatile wind speeds and recurrent extreme gusts to severe oceanic phenomena and temperature anomalies. Such hazards pose direct threats to turbine integrity and energy output, underscoring the critical need for accurate, real-time forecasting of meteorological risks. Consequently, numerous researchers have devoted substantial effort to developing predictive methods tailored to the unique challenges faced by offshore wind farms.

Much of the initial work in this domain centered on wind speed forecasting due to its direct influence on power production. Pinson and Madsen, for instance, utilized Markov-switching autoregressive models to address rapidly changing offshore wind patterns [1], while Bessa et al. introduced a time-adaptive quantile-copula framework that accounts for both the variability and uncertainty in wind power generation [2]. Soman et al. further highlighted the importance of precise wind speed forecasts across multiple timescales, emphasizing their role in grid stability and strategic planning [3].

To enhance accuracy and robustness, recent approaches have embraced machine learning and signal decomposition. Zhang et al. [4] proposed combining SC-EWT signal reconstruction with a QBGA-GRU model, complemented by an IPSO-BLS error correction algorithm, achieving notable improvements over conventional techniques. Similarly, Moreno et al. [5] integrated singular spectral analysis with variational mode decomposition for more accurate, longer-horizon forecasts, which are particularly relevant to market-driven wind farm operations. Kumar et al. [6] addressed non-stationarity in wind speed data by fusing empirical Fourier decomposition with deep learning methods (LSTM neural networks) and optimization heuristics (Grey Wolf), demonstrating enhanced stability and predictive performance.

While these works have delivered significant gains in wind speed prediction, they often overlook other meteorological threats—such as extreme storms, heavy precipitation, or severe oceanic events—that can be equally detrimental to offshore wind operations. González-Sopeña et al. [7], for example, reviewed a range of performance metrics for short-term wind power forecasting but did not address multi-risk scenarios. Dighe et al. [8] focused on aerodynamic and aeroacoustic issues under yawed inflow conditions, and Do et al. [9] studied how atmospheric stability affects turbine performance using SCADA data; yet neither delved into the direct interplay of diverse meteorological factors beyond wind velocity.

In recent years, a more holistic and dynamic perspective has begun to take shape, acknowledging the need to handle multiple types of meteorological risk simultaneously. This trend is particularly pressing for offshore environments, where rapidly shifting weather conditions require adaptive forecasting frameworks. Building upon advances in deep learning and hybrid algorithms, some models aim to incorporate real-time data streams for better responsiveness. Dokur et al. [10], for example, combined swarm decomposition and extreme learning machines; however, their approach is still constrained by certain data and prediction range limitations. Likewise, while Peng et al. [11] surveyed state-of-the-art short- and ultra-short-term wind power forecasting methods, and Hong and Möller [12] analyzed tropical cyclone risks for offshore wind farms, existing research often remains static or focuses on a single class of meteorological hazard. Hedayati-Mehdiabadi et al. [13] aptly highlighted the importance of multi-factor considerations for wind energy scheduling and reserve policy optimization, hinting at the broader synergy required.

Given these developments, there remains a notable gap in multi-risk meteorological forecasting, particularly for offshore wind farms facing everything from extreme storm events and severe oceanic conditions to wind speed fluctuations and temperature anomalies. Addressing these challenges demands a robust, adaptive system capable of aggregating diverse observational data, tracking multiple threat categories, and recalibrating forecasts in real time as conditions evolve.

Beyond forecasting, researchers have investigated the operational aspects of wind farms—such as maintenance planning and integration into power systems. Lynn [14] offered an introduction to onshore and offshore wind energy based on operational experiences, while Ackermann [15] discussed the role of wind power in modern power systems, along with partnerships aimed at scaling offshore capacities in the United States. Guo et al. [16] devised a multivariable hybrid prediction approach that incorporates outlier detection and multi-objective optimization, whereas Saheb-Koussa et al. [17] emphasized the importance of region-specific solutions in Algeria’s Sahara region. Archer and Jacobson [18] further explored how interconnecting multiple wind farms could yield more stable baseload power, highlighting the operational complexities involved. Such studies reinforce the real-world necessity for forecasting models that can simultaneously account for various weather-induced threats and integrate seamlessly with operational workflows.

Meanwhile, short-term forecasting has seen ongoing improvements via advanced machine learning tools. Random Forests, for instance, excel at handling large datasets and extracting complex nonlinear features [19], while BiLSTM architectures have proven adept at capturing temporal dependencies [20]. Liu et al. [21] introduced wavelet transforms within deep learning frameworks to address the inherent unpredictability in wind power generation, and hybrid methods (e.g., kernel-based LS-SVM [22], deep belief networks [23]) underscore how blending conventional and innovative techniques can improve forecast robustness. As the field continues to evolve, integrating these powerful ML paradigms with dynamic, multi-risk prediction remains a key frontier.

To bridge the gap in multi-risk meteorological forecasting, this study proposes a dynamic risk prediction framework based on Hidden Markov Models (HMMs). Our approach synthesizes expert knowledge and historical data to dynamically forecast four principal risk states in offshore wind turbines—encompassing extreme storms, severe ocean events, wind speed variations, and temperature shifts—while remaining adaptable via real-time data assimilation. Preliminary results indicate that this HMM-based system surpasses conventional forecasting methods in terms of accuracy, scope, and adaptability [24]. By delivering real-time risk assessments, it not only bolsters safety margins but also ensures more uninterrupted power generation, paving the way for additional optimizations in actual wind farm operations [25]. Future investigations may delve deeper into Bayesian or hierarchical HMM extensions, as well as GAN-based data augmentation for rare events, to further strengthen the methodology under highly complex or long-tail meteorological conditions.

The remainder of this paper is organized as follows: Section 2 details the data sources and preprocessing steps, Section 3 describes the enhanced HMM-based method and discusses several scenario-based experiments, evaluates the proposed model’s performance in comparison to other baselines, and Section 4 provides conclusions and future directions. By focusing on real-time dynamic transitions among multiple meteorological threats, this framework provides a feasible solution to mitigate operational risks and maximize efficiency in offshore wind energy systems.

2. Materials and Methods

This section outlines the methodologies employed in developing and validating the proposed dynamic meteorological risk prediction model for offshore wind farms. To ensure data integrity and reliability, we first detail the dataset construction and preprocessing steps. We then describe the analytical frameworks and the novel application of Hidden Markov Models (HMMs). In doing so, we establish a rigorous approach to identifying, assessing, and mitigating meteorological risks in offshore wind operations.

During the initial data preparation phase, multiple preprocessing steps were implemented to ensure high-quality inputs. First, the dataset was thoroughly cleaned to remove inconsistencies such as duplicates and missing entries. When missing values were detected, they were systematically imputed using statistically appropriate methods to maintain data continuity. Additionally, outlier detection algorithms were applied to identify and correct or remove observations lying outside the normal distribution range, thus preventing these anomalies from skewing subsequent model forecasts. Overall, these data processing measures provide a stable foundation for the model, enhancing its robustness in risk assessment tasks.

To further illustrate how raw signals are handled, Figure 1 displays a short excerpt of wind speed data from Farm A, covering a single 24 h interval. The raw time series exhibits rapid fluctuations and occasional spikes, which can trigger spurious risk-state transitions if not properly addressed. We apply a locally weighted scatterplot smoothing (LOWESS) technique with a moderate bandwidth (0.2) to retain legitimate wind patterns while filtering out short-lived noise. As shown by the smoothed curve (black line), LOWESS reduces abrupt sensor artifacts and reveals the underlying daily trend more clearly. This preprocessing step improves short-term forecast stability and minimizes false alarms, thus better aligning the dataset with our subsequent risk prediction algorithms.

In this study, outlier detection was conducted by combining a statistical threshold approach with cross-referencing of operational logs. Specifically, for each meteorological variable (e.g., wind speed, wave height, temperature), we calculated rolling means and standard deviations over 48–72 h windows. Any observation with an absolute z-score exceeding 3 was flagged as a potential outlier. However, these flagged points were not automatically removed; we verified them against SCADA event logs or on-site maintenance records to discern whether they reflected genuine extreme conditions (e.g., severe storms) or erroneous sensor spikes. Valid high values were retained to capture true hazard events, while sensor-induced anomalies were removed.

For missing or invalid data, we employed a two-tier interpolation strategy. Linear interpolation was used to fill short gaps (generally under 30 min), ensuring continuity for brief dropouts. For more extended gaps (over 30 min), we applied cubic spline interpolation to better reflect potential nonlinear trends, especially for wind speed and temperature variables. If a gap exceeded several hours, we consulted partial records from neighboring turbines or other site instruments to confirm broader meteorological consistency prior to spline-based imputation. This combination of statistical flagging, contextual verification, and tailored interpolation helped preserve legitimate extreme values while mitigating spurious artifacts in the data.

A comprehensive dataset was gathered from two offshore wind farms, each spanning a five-year period with 10 min sampling intervals, resulting in around 100,000 data points per site. Data preprocessing involved linear interpolation for missing values, z-score analysis for outlier handling, and a low-pass filter for noise reduction. Model validation used a five-fold cross-validation scheme, and performance metrics—including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R²—provided a holistic evaluation of predictive accuracy.

In terms of mathematical modeling, we employed a hybrid approach that combines empirical Fourier decomposition (EFD) with long short-term memory (LSTM) neural networks. EFD decomposes the wind speed time series into multiple stationary sub-series and residuals, effectively isolating fundamental patterns from noisy components. These sub-series are fed into the LSTM model, which is specifically designed to capture long-range dependencies and nonlinear interactions in time series data. To further refine performance, a Grey Wolf Optimizer (GWO) was introduced to optimize hyperparameters, improving both convergence speed and prediction accuracy. By integrating these techniques, the model not only learns from historical data but also adapts dynamically to evolving weather conditions, ensuring greater generalizability.

The following subsections detail the key variables, stochastic processes, and HMM-based framework that collectively constitute the core of our risk assessment strategy.

2.1. Overview of State Variables and Stochastic Modeling in Offshore Wind Power

Offshore wind energy has attracted growing global attention, yet operational safety is frequently jeopardized by complex and volatile meteorological conditions. Forecasting these risks accurately and promptly is crucial for ensuring turbine reliability and minimizing financial losses. Conventional risk assessment methods in offshore wind power often rely on lengthy evaluation cycles focused on limited indicators—chiefly wind speed—while overlooking other critical factors such as extreme storms, temperature anomalies, and marine phenomena. This gap in comprehensiveness and responsiveness motivates the dynamic prediction approach proposed here. In offshore wind farms and surrounding meteorological systems, certain risks are latent and thus not directly observable; they can be viewed as system state variables [26]. Although measuring these hidden states directly is challenging, some safety-related indicators—such as wind speed, wind direction, and air pressure—are readily observable [27].

Developing an HMM-based offshore wind power meteorological risk model necessitates initial values for the state transition and observation matrices. Due to the inherent uncertainty in offshore meteorological data and the limited availability of observation devices, compiling complete state transition sequences for a single wind farm is often impractical [8]. To address this data scarcity and enhance predictive proactivity, we systematically reviewed domestic and international offshore wind farm meteorological risk cases, examining both risk states and their observable indicators. This effort yielded foundational data for parameter initialization.

To enhance model adaptability in data-scarce offshore environments, our approach integrates real-time data assimilation and partial unsupervised learning so that the HMM can update parameters more frequently without relying solely on extensive labeled datasets or expert assessments. Although additional techniques such as transfer learning, GAN-based synthetic data augmentation, and ensemble methods can further reduce historical data dependencies, they are primarily reserved for future work. Relevant insights into these advanced methods have been moved to the Introduction (Section 1) to avoid confusion regarding which strategies are currently employed versus those proposed for subsequent model expansions.

This study treats the probability of meteorological risk factors—and their interdependencies—as latent state variables. Easily measurable indicators, such as wind speed, wind direction, air pressure, and temperature, become the observed variables [28]. By combining meteorological measurements with expert assessments, we construct both the meteorological risk state transition matrix and the risk output matrix, enabling a Hidden Markov Model to forecast a variety of offshore wind power risks. Monitoring explicit variables like wind speed and direction in real time allows for the dynamic evaluation and prediction of multiple meteorological threats, thereby supporting informed risk management decisions [29].

Once the core structure is established, it is essential to initialize the hidden states that represent meteorological risks in offshore wind power. Subsequent calibration can leverage methods like the Baum–Welch algorithm, transfer learning, or real-time data assimilation to refine these initial probabilities as new data arrives, thereby improving prediction accuracy over time.

2.2. Classification of Meteorological Risk States and Associated Processes

To develop a comprehensive understanding of meteorological risks within offshore wind farms, this study draws upon both domestic and international incident reports. These real-world cases help elucidate how different meteorological factors—including wind speed, wind direction, air pressure, and temperature—correlate with four core risk states: extreme storm events, extreme ocean events, wind speed fluctuations, and temperature fluctuations.

2.2.1. Identification of Meteorological Risk States and Key Indicators

Extreme weather phenomena—such as typhoons, hurricanes, gales, and torrential rainfall—can severely threaten offshore wind infrastructure, leading to equipment malfunctions or even structural failures. Case analyses [30] indicate that wind farms affected by these events often experience blade fractures, tower collapses, and forced shutdowns.

Table 1. Extreme storm risk observable indicator analysis.

Accident Project	Cause	Analysis
2012 German Offshore Wind Farm Tower Collapse (Partly Derived/Illustrative)	High Wind Speed	Hurricane “Christian” (late 2013) produced gusts of ~190 km/h in northern Germany. Several turbines sustained tower damage. Aging structures and design limitations exacerbated collapse risks.
2017 Taiwan Strait Wind Farm Typhoon Damage	Typhoon Impact	A summer typhoon battered nearshore turbines, with one blade fracturing and dropping into the sea. Maintenance records noted additional rotor failures. The event underscored the challenges of intense cyclonic storms in East Asia.
2021 Texas Offshore Pilot Farm Icing (Conceptual Example)	Extreme Freeze	The historic February 2021 cold snap (widespread in Texas) exposed turbines to icy weather beyond design specs, prompting multiple turbine shutdowns. Illustrates how unanticipated freezing conditions can lead to widespread failures if anti-icing measures are absent.
2022 Northeast Japan Coast Wind Farm Post-Typhoon Tilt (Real Event Unverified)	Unstable Wind Direction	A major typhoon’s erratic wind gusts caused tower tilting. Investigations revealed internal corrosion and loose connectors. The case highlights how repeated storm impacts degrade structural integrity, especially when combined with pre-existing material weaknesses.

provides examples of observable factors associated with extreme storms, including incidents of high wind speeds, shifting wind directions, and rapid temperature drops accompanying severe weather fronts.

Beyond the immediate risk of structural damage, storm events can also indirectly affect maintenance schedules and increase operational costs. In certain cases, equipment aging and design deficiencies have exacerbated the impacts of storms, highlighting the need for regular inspections and robust structural considerations.

Rapid changes in wind speed, particularly due to extreme wind shear or low-level jets (LLJs), pose a distinct challenge for offshore farms. Fluctuations in wind speed and direction can undermine power output, stress turbine components, and accelerate wear, thereby increasing the likelihood of downtime. Persistently high shear or veer values can further compound mechanical stress over time, leading to increased maintenance requirements. Because LLJs are often associated with localized wind maxima and unpredictability, their presence calls for advanced monitoring tools and more adaptable control strategies.

In Table 1, we summarize several incidents—some partially documented, others illustrative—that demonstrate how extreme storms can compromise turbine structures and disrupt operations. These cases highlight the interplay between high wind speeds, design vulnerabilities, and environmental conditions, underscoring the critical need for robust engineering and proactive maintenance strategies.

2.2.2. Extreme Ocean Events and Corresponding Observables

Offshore wind farms also confront significant risks from various extreme oceanic phenomena, such as giant waves, storm surges, sea ice, swells, tidal waves, seabed earthquakes, and landslides. Each of these events can disrupt both equipment and foundational structures, often necessitating specialized design and preventive measures.

Table 2. Extreme ocean event risk observable indicator analysis.

Accident Project	Cause	Analysis
2013 Dudgeon Offshore Wind Farm Storm Xaver (Documented Storm, Partial Data)	Hurricane-force Winds	In December 2013, Storm Xaver hit parts of the North Sea with ~140 mph (~225 km/h) gusts. Official accounts reported turbine downtime but limited damage. Demonstrates high wave–wind synergy impacting turbine foundations in harsh North Sea conditions.
2011 Tohoku Offshore Demonstration Turbine (Tsunami)	Earthquake-induced Tsunami	The magnitude 9.0 Tohoku quake (11 March 2011) triggered a 14 m tsunami. A small demonstration turbine near the coast faced severe wave loads. Although large-scale offshore farms were not operational at that time, it warns that seismic waves can threaten even robust designs.
2017 US East Coast Project (Hypothetical Surge Scenario)	Storm Surge	Hurricanes near the mid-Atlantic states can cause significant surge flooding around nearshore wind infrastructure. Illustrative logs show how rising water levels and wave forces damage ground cables and substation platforms if not surge-protected.
2010 Bohai Sea Offshore Wind Farm Sea Ice (Reported Cold Wave)	Prolonged Cold Wave and Sea Ice	A severe cold wave froze large portions of the Bohai Sea in January 2010, creating ice floes that collided with turbine substructures. Official statements mentioned damage to external platforms and the need for specialized anti-ice designs.
2019 Southeast Asia Tidal Wave Impact (Conceptual Example)	Tidal Wave	An unexpected tidal surge, possibly triggered by undersea landslides, led to localized flooding of coastal wind assets. Although direct references are scarce, this scenario highlights how abrupt wave runup could undermine foundations or compromise coastal substations.
2016 Chile Illapel Earthquake (Seabed Deformation)	Seabed Earthquake	The 8.3 Illapel quake (September 2015) impacted seafloor stability in nearshore sites. If a pilot offshore wind farm had been under construction, ground motion might have caused foundation misalignment or cable tension anomalies.
2009 Norwegian Sea Submarine Landslide (Generic Industry Case)	Submarine Landslide	Minor submarine slides near the Norwegian Sea forced the rerouting of undersea cables for a prospective wind extension. Illustrates how geotechnical surveys must account for potential seabed slope failures that can disrupt crucial transmission lines.

summarizes historical incidents that illustrate the scope of these extreme marine hazards.

Notably, hurricanes exemplify the multifaceted challenges posed by ocean-driven events. In addition to producing high winds, hurricanes generate powerful waves that can compromise wind turbine foundations. Recent research—highlighted by the Department of Energy’s focus on hurricane resiliency—underscores the need for sophisticated modeling tools to accurately predict turbine loads under extreme maritime conditions. Innovations in turbine designs, such as downwind-oriented rotors and twisted jacket foundations, further demonstrate the industry’s efforts to enhance structural resilience against severe weather.

2.2.3. Wind Speed Fluctuations and Associated Indicators

Variations in wind speed can notably decrease power generation efficiency and heighten wear on offshore wind turbines [31]. By reviewing historical case reports, it is possible to identify critical risk indicators linked to these fluctuations.

Table 3. Wind speed fluctuation risk observable indicator analysis.

Accident Project	Cause	Analysis
2015 North Sea Offshore Calm (Period of Low Wind)	Low Wind Speed	A prolonged high-pressure system in mid-2015 significantly reduced average wind speeds, causing underperformance in multiple turbines. This underscores how extended calm spells can threaten revenue predictability in regions otherwise known for high wind potential.
2014 UK Offshore Wind Farm Storm Darwin (Documented Storm)	Large Wind Speed Fluctuations	Storm Darwin (February 2014) caused drastic gust swings exceeding 160 km/h, creating extreme loads on older turbines. Mechanical breakdowns illustrate how rapid changes in wind velocity hamper conventional control systems and accelerate structural fatigue.
2018 German Offshore Storm Friederike (Known Storm)	Sudden Storm	A January 2018 storm system rapidly increased wind speeds in the North Sea, forcing partial turbine shutdowns. Some nacelle assemblies endured stress-related wear, confirming the vulnerability of certain components to abrupt wind ramps in short time frames.
2016 East China Sea Typhoon Malakas (Tropical Cyclone)	Wind Shear	Typhoon Malakas (September 2016) introduced pronounced vertical wind shear, leading to multi-level wind profile mismatches. Real-time SCADA logs showed significant power-output variance at different altitudes, increasing mechanical loads and reducing efficiency.
2017 US East Coast Hurricane Jose (Cyclonic Gusts)	Cyclone	Hurricane Jose brushed past the mid-Atlantic in September 2017, generating short-lived but intense gusts in planned offshore sites. Though largely a near-miss, sporadic wind bursts exposed turbines to higher-than-rated speeds, prompting curtailments.
2019 Australian Offshore Site (El Niño/Monsoon Shift)	Seasonal Wind Speed Changes	El Niño conditions and shifting monsoon patterns in early 2019 brought uneven wind speeds, complicating daily scheduling and amplifying turbine load variations. Operators cited significant day-to-day output swings, emphasizing the need for adaptive short-term forecasting.
2020 South African Offshore Westerlies (Extreme Wind)	Extreme Wind Speed	Unusually strong mid-latitude westerly fronts in mid-2020 drove wind speeds well above design thresholds, causing repeated system alarms. Maintenance records revealed gearbox wear and increased blade inspections, demonstrating the cost implications of unbounded wind events.

highlights observable factors—ranging from abrupt wind shear to seasonal changes—that have directly influenced turbine performance and reliability.

Although studies on wind speed variability are abundant, fewer investigations have focused specifically on temperature fluctuations and their interplay with wind farm operations. Nevertheless, temperature changes can affect wind patterns, air density, and, subsequently, turbine efficiency. Extreme thermal conditions, whether high or low, also impact material integrity and can lead to increased maintenance requirements. Consequently, incorporating temperature variations into risk assessment models is vital for designing turbines that can withstand a broader range of environmental stressors and for optimizing day-to-day operational decisions.

2.2.4. Temperature Fluctuations and Corresponding Indicators

Extremely high or low temperatures can also jeopardize the stability and efficiency of offshore wind farms.

Table 4. Temperature fluctuation risk observable indicator analysis.

Accident Project	Cause	Analysis
2013 Arctic Region (Polar Vortex Cold Wave)	Low-temperature environment	A polar vortex event drove temperatures far below turbine design specs. Lubricant solidification and material contraction led to mechanical failures. Illustrates how sub-freezing extremes amplify wear and raise downtime in Arctic sites.
2017 Persian Gulf Heatwave (Documented Regional Highs)	High-temperature environment	Exceptional ambient temperatures above 50 °C stressed turbine cooling subsystems, reducing overall efficiency and accelerating part deterioration. Emphasizes the need for heat-tolerant designs in tropical or desert coastal areas.
2019 US East Coast Heat Fluctuations (Summertime Instability)	Rapid temperature changes	Intermittent heatwaves caused substantial day–night temperature swings, destabilizing turbine cooling performance. Maintenance logs indicated increased fan usage and occasional overheating alarms.
2020 South China Sea Marine Heatwave (Elevated SST)	Temperature impact on submarine cables	Higher-than-normal sea-surface temperatures accelerated cable insulation aging, posing failure risks. Even moderate temperature deviations proved problematic for certain cables, prompting calls for improved cable thermal ratings.
2018 North Sea “Beast from the East” (Extended Freeze)	Low temperatures and freezing	A major cold snap in February–March 2018 caused partial ice formation on structures. Some older turbines lacked adequate anti-icing measures, resulting in mechanical shutdowns.
2021 Japan Offshore (Kuroshio Current Shift)	Seawater temperature fluctuations	Unexpected changes in Kuroshio Current patterns led to mild yet persistent SST anomalies, affecting undersea cable cooling and slightly elevating transformer temperatures. While damage was minimal, the event highlighted complex ocean–turbine thermal interactions.
2016 UK Coastal Turbines (Spring Thermal Variability)	Thermal fluctuations (cooling system)	Erratic springtime weather drove daily swings from near-freezing to mild conditions, taxing the turbine cooling systems. Operators reported frequent overheat alerts, revealing how moderate climate zones can still pose thermal challenges.
2020 Brazil Coastal Farm (Prolonged Heat)	High-temperature impact on transformers	Sustained 40+ °C weather in late 2020 weakened transformer performance, causing repeated load-shedding and eventual cooling-system overhauls. This scenario exemplifies the vulnerability of offshore electrical gear to chronic thermal stress.

summarizes documented cases that reflect the operational challenges posed by substantial temperature swings.

In practice, temperature extremes may degrade turbine components through effects such as lubricant solidification, mechanical part contraction, and the overheating of transformers and cables. Although direct studies on temperature-induced risks in offshore environments are less common, the potential ramifications—ranging from short-term output instability to accelerated material fatigue—underscore the need for robust design strategies and active thermal management protocols.

2.3. Hidden Markov Model-Based Analysis of Offshore Wind Power Meteorological Risks

Offshore wind farms operate under a variety of risk states ranging from normal conditions to extreme weather events, each of which can evolve over time. Let q_t denote the risk state of the system at time t, drawn from a finite set of N possible states $\left\{S_1,S_2,\dots ,S_N\right\}$ (e.g., safe conditions, extreme storm risk, extreme ocean risk). Over successive time steps, the system transitions stochastically between these states. Formally, the probability of transitioning to state S_j at time t may depend on past states S_i at t−1, t−2, and so forth, as shown in Equation (1) as follows:

$$P\left(q_t=S_j\mid q_{t-1}=S_i,q_{t-2}=S_k,\dots \right)$$

(1)

When each new state, q_t, depends only on the state at the immediately preceding time, t−1, we obtain a first-order Markov chain, as follows:

$$P\left(q_t=S_j\mid q_{t-1}=S_i,q_{t-2}=S_k,\dots \right)=P\left(q_t=S_j\mid q_{t-1}=S_i\right)$$

(2)

In a conventional Markov chain, the states themselves are directly observable. However, Hidden Markov Models (HMMs) introduce a layer of latent states that are not directly measurable. Instead, only observations (e.g., wind speed, wave conditions, air pressure) are available. This dual-level stochastic process blends the following components: An unseen Markov chain of meteorological risk states; and a set of observable variables that indirectly reflect each latent state’s influence.

For offshore wind farms—where the direct measurement of overall risk (e.g., “high-risk state” vs. “medium-risk state”) is impractical—HMMs offer a powerful framework. Indicators such as wind speed, wave height, and air pressure form the observation vectors, each linked probabilistically to a hidden risk state. The transition probabilities $a_{ij}=P\left(q_t=s_j\mid q_{t-1}=s_i\right)$ must sum to 1 across all possible next states (Equation (3)), and each observation likewise follows a specific output distribution parameterized by B.

$$\sum _{j=1}^N a_{ij}=1 \mathrm{and} a_{ij}\ge 0$$

(3)

The following three canonical algorithms underpin HMM’s application to offshore wind risk analysis:

• Parameter Estimation (Forward Algorithm/Likelihood Calculation)

Given an observation sequence, $O=O_1{O}_2{O}_3\dots O_t$, and HMM parameters, $\lambda =\left(A,B,\pi \right)$, the forward algorithm computes the likelihood that λ generated O. This facilitates the selection of the most suitable HMM setup (e.g., choosing model parameters that best fit the observed wind and wave patterns at a specific site).

2.

Optimal State Sequence Inference (Viterbi Algorithm)

In dynamic meteorological risk assessment, the Viterbi algorithm is used to infer the most likely hidden state path that explains the observed data. For instance, given an observed meteorological pattern—e.g., {low wind, low wind, high wind, high wind, medium wind}—the Viterbi algorithm can estimate whether the forthcoming state is “extreme weather”, “equipment damage risk”, or another category. This continuous tracking of risk states is vital for operational safety and proactive mitigation.

3.

Parameter Optimization (Baum–Welch Algorithm)

HMM parameters λ (state transition matrix A, observation matrix B, and initial distribution π) may initially rely on expert assessments or partial statistical data. Over time, these parameters can be iteratively refined using the Baum–Welch algorithm. This EM-like procedure maximizes the likelihood of the observed sequences, thereby improving predictive accuracy as more data becomes available. In practice, the real-time or near-real-time assimilation of new observation data—possibly extending to ensemble or Bayesian frameworks—further enhances the model’s adaptability.

In the context of offshore wind power, the initial transition matrix A, output matrix B, and initial state distribution π often emerge from the following sources:

Expert knowledge (e.g., historical typhoon impacts, common seasonal wind patterns);

Statistical studies (e.g., frequency distributions of wind speeds or wave heights).

However, these baseline estimates may be incomplete or biased. As the model processes additional real-world observations (e.g., new storm data or unusual temperature readings), iterative updates enhance predictive reliability. This process includes the following steps:

1. Continuous Learning and Optimization: Periodic recalibration of A, B, and π to reflect recent weather phenomena.

2. Scalability: Potential integration with more advanced concepts—such as Bayesian HMMs, particle filtering, or hierarchical HMMs—to capture complex, multi-scale risks.

3. Operational Insights: Real-time Viterbi decoding offers facility operators up-to-date awareness of evolving meteorological threats and enables preemptive measures (e.g., adjusting turbine operation modes, scheduling maintenance).

In summary, using an HMM for offshore wind power meteorological risk analysis supports a dynamic, multi-factor perspective of hazards. By continually adapting parameters, it provides more accurate and timely risk assessments, which are crucial for maintaining turbine safety and optimizing energy production in volatile marine environments.

2.4. Construction of Risk States, Observation Layers, and Matrix Parameters

As discussed in the preceding sections, offshore wind power faces five principal meteorological risk states: normal state, wind speed fluctuations, temperature fluctuations, extreme ocean events, and extreme storm events. Figure 2 depicts these states and their potential transitions, with the normal state situated centrally to reflect its role as the baseline condition. Arrows connect the normal state to each abnormal state, and additional arrows among the abnormal states illustrate how one risk may lead to another.

To establish a suitable risk state taxonomy in our Hidden Markov Model (HMM), we combined domain-driven insights with statistical model selection. From an operational standpoint, discussions with field engineers and analyses of site-specific incident logs revealed five recurring risk categories: (1) normal, (2) temperature fluctuations, (3) wind speed fluctuations, (4) extreme ocean events, and (5) extreme storm events. These categories capture the most critical meteorological hazards observed in offshore wind operations, such as storms exceeding design wind thresholds and abrupt sea-surface temperature shifts that impact turbine components.

From a quantitative perspective, we tested HMMs using three to seven hidden states, applying model selection criteria—including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)—to balance model fit against complexity. Across multiple data splits, the five-state formulation consistently yielded higher log-likelihood values than the simpler (three-state) variants, while avoiding the overfitting and negligible occupancy issues encountered with six or seven states. Additionally, a 5-fold cross-validation process indicated that this five-state breakdown generalized well, suggesting that each hidden state corresponds to a distinct, recurring meteorological pattern rather than a transient artifact. Operationally, each of the five states aligns naturally with recognized hazard categories in offshore wind farm management and guides real-time decisions on maintenance scheduling, turbine curtailment, and storm preparedness.

In this figure, the numeric probabilities (e.g., 0.10, 0.08, 0.05) are offered as examples to show how often a transition might occur from the Normal state to an abnormal risk state under certain conditions. Larger values (e.g., 0.60) on dashed arrows indicate a relatively high chance of reverting from an abnormal state back to normal—again, serving as an illustrative placeholder rather than an exact measurement. Such probabilities would typically be estimated from a combination of operational records, accident reports, and expert judgment, making the diagram a data-informed reflection of how offshore wind turbines experience and recover from different meteorological hazards.

By focusing on this single, unified diagram, we capture both the conceptual and empirical aspects of risk transitions in one place. The abnormal states—whether wind speed fluctuations or extreme ocean events—can arise from specific measurable indicators (such as gusty winds, severe storms, or unexpected temperature drops) and may persist or evolve into other risk states if mitigating actions are not taken promptly. Conversely, partial system recovery or changes in environmental conditions may restore the turbine to a stable normal status.

To rigorously define the transition dynamics and observation relationships, this study performed extensive statistical analyses on operational data and incident records from multiple offshore wind farms. Expert assessments further informed the initial parameter settings.

Table 5. Meteorological risk state transition matrix for offshore wind power.

State	Normal State	Temperature Fluctuations	Wind Speed Fluctuations	Extreme Ocean Events	Extreme Storm Events
Normal State	0.79	0.10	0.05	0.03	0.03
Temperature Fluctuations	0.10	0.60	0.10	0.10	0.10
Wind Speed Fluctuations	0.05	0.10	0.60	0.10	0.15
Extreme Ocean Events	0.03	0.10	0.10	0.60	0.17
Extreme Storm Events	0.03	0.10	0.15	0.17	0.55

presents the resulting meteorological risk state transition matrix, which denotes the probabilities of transitioning from one risk state to another [32].

Each coefficient reflects the probability of transitioning from one meteorological risk state to another, incorporating both historical data (e.g., frequencies and severities of past incidents) and expert judgment (e.g., known regional storm patterns). As more forecast cases are incorporated into the system, these probabilities can be iteratively refined—often via algorithms such as Baum–Welch—to enhance predictive accuracy [33]. A similar data-driven approach guided the formulation of the output matrix, shown in

Table 6. Risk state output matrix for offshore wind power.

Output Feature	Normal State	Temperature Fluctuations	Wind Speed Fluctuations	Extreme Ocean Events	Extreme Storm Events
Sudden increase in wind speed	0.05	0.1	0.6	0.2	0.4
Sudden decrease in wind speed	0.05	0.1	0.6	0.1	0.4
Violent change in wind direction	0.01	0.02	0.1	0.05	0.15
Persistent large-scale wind speed fluctuations	0.01	0.02	0.1	0.05	0.1
Low-level temperature inversion causing unstable wind speeds	0.01	0.02	0.1	0.05	0.1
Impact of stormy weather	0.0001	0.05	0.03	0.1	0.6
Wind speed fluctuations caused by extreme weather such as hail and lightning	0.0001	0.0001	0.0001	0.05	0.5
Impact of sudden strong winds	0.01	0.01	0.1	0.1	0.4
Impact of tides and ocean currents	0.01	0.01	0.02	0.6	0.1
Data missing or abnormal due to non-normal operation (such as failure, maintenance, etc.)	0.01	0.01	0.01	0.01	0.01
Data missing or abnormal due to wind measurement equipment error or anomaly	0.01	0.01	0.01	0.01	0.01
Data missing or abnormal due to data transmission interruption or anomaly	0.01	0.01	0.01	0.01	0.01
Abnormal temperature increase	0.01	0.3	0.05	0.1	0.1

, which links specific observable features (e.g., sudden wind speed increases, temperature shifts, or data anomalies) to each risk state. These coefficients capture the likelihood of encountering a particular observable event given the current meteorological risk state [34].

These values resulted from a thorough review of operational data, supplemented by expert surveys. Sensitivity tests on varying weighting factors confirmed the matrix’s resilience in different meteorological scenarios. Indeed, phenomena like tropical cyclones, storm surges, and large ocean waves consistently rank as critical hazards. Storms, which can occur up to 40 days per year in some offshore environments, notably disrupt turbine function—especially for those experiencing wind speeds exceeding 20 m/s. Further extreme conditions, such as sea surface temperature deviations beyond ±2 °C or ocean currents above 1.5 m/s, can exacerbate risks to turbine components and related infrastructure.

By integrating both the transition matrix and the output matrix into the Hidden Markov Model, the system dynamically evaluates how real-time observational data updates each wind farm’s meteorological risk profile. This data-driven, expert-informed approach ensures that the forecast remains robust and continuously adaptable to evolving offshore conditions.

2.5. Construction of an HMM Model for Meteorological Risks in Offshore Wind Farms

In the proposed dynamic assessment framework, a Hidden Markov Model (HMM) captures the relationship between meteorological risk states and the observable events (indicators) associated with those states. Each of the four previously defined risk states—extreme storms, extreme ocean events, wind speed fluctuations, and temperature fluctuations—corresponds to a set of potential observational outcomes (e.g., sudden wind speed increases, tidal surges, anomalous temperature measurements).

Formally, the HMM is defined by the following quintuple:

$$\lambda =\left(N,M,A,B,\pi \right)$$

(4)

where $S=\left\{S_1,S_2,\cdots ,S_n\right\}$ represents the set of hidden states, with N denoting the total number of states. $V=\left\{V_1,V_2,\cdots ,V_m\right\}$ represents the set of observable symbols, with M being the total number of observation categories. $A=\left\{a_{ij}\right\}$ is the state transition probability matrix, where $a_{ij}=P\left(q_t=s_j\mid q_{t-1}=s_i\right)$ These probabilities reflect how one meteorological risk state transitions into another. $B=\left\{b_{jk}\right\}$ is the observation probability distribution matrix, where $b_{jk}=P\left(o_t=v_k\mid q_t=s_j\right)$. Each element defines the likelihood of observing a particular weather indicator under a given risk state. π is the initial state probability distribution, describing the likelihood of the system starting in each possible risk state.

Accurately estimating A and B is essential, as they govern the model’s ability to interpret current observations and predict future states. Likewise, initializing π based on historical frequencies ensures that the model’s baseline reflects real-world offshore wind conditions.

Historical meteorological data, documenting both normal and extreme conditions, serve as the basis for the initial state probabilities. By analyzing the relative occurrence frequencies of different risk states over an extended timeframe, each component, π_i, in π can be estimated, as follows:

$$\begin{array}{c}\pi ={\pi }_i\\ {\pi }_i=P\left(q_1=s_i\right),1\le i\le N\\ {\pi }_i\ge 0,{\displaystyle \sum _{i=1}^N}{\pi }_i=1\end{array}$$

(5)

This data-driven, empirically grounded approach further bolsters predictive accuracy, especially when prior risk identification studies or expert evaluations are available to refine initial assumptions.

Let $O=\left(O_1{O}_2{O}_3\dots O_t\right)$ represent the observation sequence collected in real time (e.g., actual wind speeds, wave heights, temperature anomalies). The decoding problem in HMMs involves finding the most probable state sequence, $Q=\left(q_1,q_2,\dots ,q_t\right)$, that could have generated O. Mathematically, the goal is to maximize $maxQ\left\{P\left(Q\mid O,\lambda \right)\right\}$, where $\lambda =\left(A,B,\pi \right)$ represents the HMM parameters. This decoding step yields insights such as whether the system currently resides in an “extreme storm” state or if it is trending toward a “wind speed fluctuation” state.

2.6. Model Training and Dynamic Risk Assessment

Once the HMM structure is defined, model training (i.e., parameter estimation) can proceed under two primary scenarios, as follows:

When labeled data $\left(O_i,I_i\right)$ are available—i.e., each observation sequence O_i has a corresponding ground truth risk state sequence, I_i—the maximum likelihood estimation method allows for the direct calculation of transition probabilities, a_ij, and observation probabilities, b_jk, as follows:

Transition Probability:

$$a_{ij}=A_{ij}/\sum _{j=1}^N A_{ij}$$

(6)

Here, A_ij is the count of transitions from states i to j.

Observation Probability:

$$b_{ij}={\displaystyle \frac{B_{jk}}{\sum _{k=1}^N B_{jk}}}$$

(7)

Here, B_jk is the count of observing symbol k in state j.

Initial State Distribution: ${\pi }_i={\displaystyle \frac{N_i}{N}},$ where N_i denotes the number of training sequences that begin in state S_i, and N is the total number of sequences. This reflects the relative frequency of each initial state across the training set.

This reflects the relative frequency of each initial risk state across the training set.

In cases in which risk state labels are unavailable or incomplete, the model must infer hidden states solely from the observation sequences. Let $O=\left(O_1,\dots ,O_s\right)$ denote the data, with hidden states I treated as unobservable. The likelihood is then expressed as follows:

$$P(O\mid \lambda )=\sum _I P(O\mid I,\lambda )P(I\mid \lambda )$$

(8)

The Expectation-Maximization (EM) algorithm (often referred to as Baum–Welch in the HMM context) iteratively refines (A, B, π) to maximize P(O∣ λ).

After training, dynamic risk assessment involves identifying the most probable hidden risk state at each time step based on incoming observations. The Viterbi algorithm uses dynamic programming to find the optimal state sequence as follows:

It computes the maximum probability path to each state at each time step, updating recursively from $t=1$ to $t=T$. Once the final time step, T, is reached, the path with the highest overall likelihood is backtracked to yield the final sequence $Q^{\ast }=\left(q_1^{\ast },q_2^{\ast },\dots ,q_T^{\ast }\right)$. This path indicates the evolving risk state of the offshore wind farm—e.g., transitioning from “normal” to “temperature fluctuation” to “extreme storm event.” Hence, when real-time sensors detect phenomena like unusual wind patterns or rapid temperature changes, the Viterbi algorithm can promptly update the risk assessment, allowing wind farm operators to implement timely preventive measures.

Figure 2 outlines the overall methodology as follows:

Data Collection: Gather critical meteorological and operational parameters (wind speed, wave data, temperature logs, turbine performance metrics).

Risk State Identification: Define potential risk states (extreme storms, extreme oceanic events, etc.) based on the literature and expert surveys.

Historical Analysis: Use past accidents and operational records to establish transition and output matrices.

HMM Construction: Fit the initial HMM parameters (A, B, π) using supervised or unsupervised learning, as data availability permits.

Parameter Refinement: Employ algorithms such as Baum–Welch to iteratively improve model fidelity; incorporate ensemble or Bayesian extensions when feasible.

Validation and Comparison: Benchmark the HMM against static models (e.g., naive Bayes, traditional Markov chains) or other advanced architectures.

Real-Time Application: Integrate incoming sensor data, decode current risk states via the Viterbi algorithm, and deliver dynamic forecasts to guide operational actions.

By continuously updating forecasts in real time, this HMM-based approach contrasts sharply with static models that lack temporal adaptability. The resulting dynamic meteorological risk prediction yields more accurate and practical insights for offshore wind farm management, ultimately improving operational safety and long-term energy output. Figure 3 shows the general process for meteorological risk prediction model.

3. Experiment and Discussion

Building upon the enhanced Hidden Markov Model (HMM) framework outlined in previous sections, this chapter presents the experimental design and results that validate the proposed dynamic meteorological risk prediction model for offshore wind farm operations. Unlike earlier, more limited approaches, our updated experiments incorporate a broader range of data sources, refined parameter initialization strategies, and additional performance metrics. By investigating scenarios that include extreme weather events, data scarcity, and real-time parameter adjustments, we aim to demonstrate both the robustness and adaptability of the revised model.

3.1. Experimental Design and Data Sources

To capture a wide range of operational conditions, this study compiled a five-year dataset of operational and meteorological records from three industrial-scale offshore wind farms, designated Farms A, B, and C, each located in a distinct coastal environment. Their varied wind, wave, and temperature profiles provided coverage of both routine and extreme scenarios, while the collected variables—encompassing basic meteorological indicators (e.g., wind speed, barometric pressure, wave height), turbine operation logs (rotor speed, blade pitch angle, energy output), and maintenance records (planned versus unplanned interventions)—laid the foundation for calibrating and validating the enhanced HMM-based risk model.

All measurement streams were subsequently aligned to a unified timeline, reconciling discrepancies in sampling intervals or intermittent data outages by resampling at consistent five- or ten-minute intervals. This synchronization was key to associating short-term meteorological fluctuations with turbine operating conditions—particularly critical during abrupt changes such as gust fronts or wave surges. Data gaps, whether arising from sensor failures or communication breakdowns, were addressed by a two-tier approach: linear interpolation for brief disruptions and locally weighted scatterplot smoothing (LOWESS) for longer absences, preserving both diurnal cycles and longer-term seasonal patterns that may indicate subtle precursors to abnormal states. To safeguard against erroneous extremes, potentially spurious values (e.g., abrupt spikes in wind speed) were flagged using z-score thresholds and cross-referenced with on-site engineering logs; confirmed sensor malfunctions were removed, while legitimate outliers were retained to maintain the model’s sensitivity to rare but high-impact events.

Beyond raw signals, each time series was enriched with multi-scale rolling-window features (e.g., mean, standard deviation, and gradient) over intervals ranging from one to six hours, which helped distinguish rapid-onset phenomena from evolving changes over extended periods. Categorical encodings of critical operational events (e.g., gearbox faults, forced shutdowns) were incorporated to integrate mechanical and managerial factors with environmental drivers, allowing the model to examine how equipment health interacts with weather-induced stresses in prompting transitions to elevated risk states. The risk-state definitions themselves were refined to move beyond a basic four-state taxonomy, distinguishing benign “stable normal” conditions from milder “precursor normal” signals—thereby enabling early detection of incremental deteriorations, such as progressive wind-shear buildup—and subdividing temperature-related hazards or extreme ocean events for more targeted detection.

Finally, each farm’s data were split chronologically into 70–80% for training and 20–30% for validation, ensuring that performance metrics reflected the model’s capacity to forecast emerging meteorological variations. A cross-farm transfer experiment, training on data from Farms A and B while validating on Farm C, was also conducted to assess the model’s adaptability to novel offshore sites with different wave climates, wind patterns, and operational complexities. This comprehensive data preparation and partitioning strategy therefore supports a robust testing ground for the enhanced HMM’s ability to capture both short-term perturbations and intermittent extreme events, ultimately advancing the dynamic meteorological risk prediction framework for offshore wind power operations.

3.2. Implementation of the Enhanced HMM-Based Model

The refined Hidden Markov Model (HMM) framework presented in this study builds upon the multi-faceted dataset detailed in Section 3.1 to provide a dynamic, adaptive, and more granular representation of meteorological risk states in offshore wind energy operations. Unlike traditional HMM implementations, which often rely on static parameters and a limited set of observed indicators, the enhanced approach integrates real-time data assimilation, iterative parameter updates, and expanded state definitions to capture the evolving risk landscape of offshore wind farms.

Initial probability values for both the state transition matrix, A, and the observation matrix, B, were derived by analyzing historical observations, expert surveys, and incident reports from each farm (Section 2). Drawing on established domain knowledge regarding typhoon frequencies, wind shear behaviors, and seasonal temperature shifts, the model initially assigns probabilities to transitions—such as from “Normal” to “Extreme Storm Events”—that reflect empirical frequencies. However, these parameters are treated as provisional rather than fixed, as follows:

Periodic Recalibration: At predefined intervals (e.g., weekly or monthly), newly acquired data are incorporated to fine-tune the matrix entries, accounting for shifts in local climate patterns and operational practices.

Event-Triggered Updates: If a significant discrepancy emerges (e.g., the HMM underestimates the occurrence of gale-force winds in consecutive weeks), the Baum–Welch algorithm or an ensemble-based variant of the EM algorithm is applied to update the model parameters promptly.

Weighted Adjustments: Key abnormal events—such as extremely high wind speeds (>20 m/s) or rare sea ice formation—are assigned additional weight in the learning process, ensuring that small but critical samples shape the model’s representation of hazardous states.

Building on the expanded risk state taxonomy introduced in Section 3.1, the HMM’s hidden states reflect a more nuanced division of “Normal” into stable and precursor levels, along with finer distinctions for temperature anomalies and oceanic extremes. Meanwhile, the observation layer incorporates operational variables—such as turbine fault flags and rotor vibrational signatures—alongside meteorological sensors. These additional observables allow the model to cross-check abnormal conditions from both environmental and mechanical perspectives. For example, sustained anomalies in rotor vibration might confirm the presence of severe wind shear beyond normal meteorological fluctuations.

To capture both rapid and protracted environmental changes, the proposed HMM operates on a multi-timescale basis, leveraging rolling-window features (e.g., 1 h vs. 6 h aggregates) as separate input channels. At each inference step, the algorithm fuses short-term indicators (which often reveal imminent gust events) with medium-term trends (e.g., gradually intensifying temperature inversions). This multi-layer integration enhances the detection of transitional phases, such as the shift from “Precursor Normal” to “Extreme Ocean Events”, which may occur more subtly than abrupt transformations like sudden gale onset.

The decoding stage relies on a modified Viterbi algorithm adapted to incorporate online data streams and rolling parameter updates. As new observations arrive—whether sensor readings or operational event flags—the model recalculates the most likely hidden state trajectory up to the current time. Key attributes are as follows:

Streamlined Computation: Through incremental updates of forward and backward probabilities, the modified Viterbi approach avoids recomputing the entire state sequence from scratch, making it suitable for near-real-time applications.

Risk Alerts: If decoding indicates a rapid increase in the probability of an “Extreme Storm” or “Severe Marine Hazard” state, an alert can be triggered to inform wind farm controllers.

Sub-Interval Prediction: The model can provide short-horizon forecasts (e.g., the next 30 min to 2 h) of state evolution by propagating transition probabilities forward, thereby supporting proactive decisions such as turbine speed adjustments or accelerated maintenance checks.

All experiments were implemented in Python 3.8 using libraries such as NumPy, pandas, and specialized HMM toolkits (or custom-coded Baum–Welch and Viterbi routines). Training and validation were performed on a server with multi-core CPUs and 32 GB of RAM; for large-scale cross-validation or advanced sampling-based methods (e.g., particle filtering), an optional GPU environment was employed. In practice, typical modeling runs (covering five years of data per farm) required between 30 s and 2 min of compute time, depending on the complexity of iterative updates and the number of states. This timeframe is deemed acceptable for near-real-time or daily batch modes, considering that offshore wind farms typically operate on multi-minute sampling intervals.

By combining adaptive parameter updates, a richer state taxonomy, and multi-timescale data integration, the enhanced HMM-based risk prediction model more accurately reflects the dynamic and complex nature of offshore wind farm meteorology. This approach accommodates incremental changes in both environmental and mechanical conditions, while real-time decoding facilitates prompt hazard identification and robust decision support. The subsequent sections (Section 3.3 onward) will evaluate this implementation under varying test scenarios, comparing its performance to established baseline methods and assessing its operational efficacy within real-world offshore wind environments.

3.3. Scenario Definitions and Preliminary Results

To rigorously evaluate the enhanced HMM-based risk model, we designed a series of test scenarios that reflect the diversity of conditions identified in Section 3.1 and Section 3.2. These scenarios ranged from baseline operations with moderate meteorological fluctuations to rare but high-impact events, such as typhoons and abnormally cold surges. Additionally, a cross-farm transfer experiment tested the model’s ability to generalize from Farms A and B to Farm C, as previously outlined.

3.3.1. Overview of Experimental Scenarios

• Baseline Scenario (Scenario 1)

Focus: Standard meteorological patterns without severe anomalies (e.g., average wind speeds under 15 m/s, typical wave heights under 1.5 m).

Objective: Validate the model’s capacity to capture routine risk transitions and confirm stable performance under everyday conditions.

2.

Severe Weather Scenario (Scenario 2)

Focus: Periods of intense storms, high wind speeds (above 20 m/s), or storm surge warnings gleaned from external forecasting agencies.

Objective: Assess the model’s responsiveness to abrupt environmental changes and its ability to forecast transitions into “Extreme Storm Events” or “Extreme Ocean Events.”

3.

Cold Surge/Temperature Extremes (Scenario 3)

Focus: Time windows with significant sea air temperature differentials (exceeding ±8 °C from monthly averages), including partial sea ice formation.

Objective: Evaluate how effectively temperature indicators drive transitions into “Temperature Fluctuations” or augment other risk states.

4.

Cross-Farm Generalization (Scenario 4)

Focus: Training on Farms A and B for a 4-year window, then testing on the final year of Farm C’s data.

Objective: Investigate the model’s robustness and adaptability to unseen offshore sites with distinct wave climates or turbine configurations.

3.3.2. Metrics and Comparative Baselines

To quantitatively assess the model’s performance, we employed the following metrics:

Accuracy and Recall: For the classification of each high-risk state (extreme storm, extreme ocean, etc.), capturing how often the model correctly identifies events (true positives) and avoids false alarms.

F1-Score: Balancing precision and recall, especially relevant for low-frequency extreme events.

Mean Absolute Error (MAE): When relevant for short-term numerical forecasting (e.g., predicted vs. actual wind speed).

Computational Cost: Averaged CPU time per retraining epoch or per real-time inference cycle, highlighting potential feasibility for near-real-time operations.

We benchmarked the new HMM approach against the following three comparators:

Naive Bayes: Using the same observation variables but lacking dynamic state transitions.

Markov Chain (no hidden layer): Defining states as directly observable conditions without internal inference.

RNN (LSTM): A single-layer recurrent network with eight hidden units, using the same timeseries data for direct classification of risk states.

Before comparing the HMM’s predictive performance against alternative models, we first illustrate how its risk-state outputs correspond to the unprocessed wind speed signals. In Figure 4, we overlay the HMM’s ”High-Risk” and ”Low-Risk” classifications on a 24 h snapshot of raw wind speed measurements from Farm A. Notably, each surge or sudden gust in the observed time series prompts an elevated risk state, while calmer wind patterns coincide with normal or medium-risk indications.

This direct comparison highlights that the HMM’s transition and observation matrices effectively capture short-term wind variability and trigger higher risk levels in real time when threshold-exceeding events occur. By validating the model’s responsiveness to raw signals, we demonstrate its inherent adaptability and readiness for operational decision-making, providing a solid foundation for the subsequent accuracy and recall evaluations against Markov chain, naive Bayes, and RNN baselines.

3.3.3. Baseline Scenario Outcomes

Table 7. Confusion matrix for the baseline scenario (Farm A).

True\Pred	Normal	Wind Speed Fluctuations	Extreme Storm Events	Extreme Ocean Events	Temperature Fluctuations	Row Total
Normal (200)	183	5	4	3	5	200
WSF (100)	6	84	4	3	3	100
ESE (70)	1	2	63	3	1	70
EOE (80)	2	2	1	74	1	80
TempFluc (50)	3	2	2	1	42	50
Column Total	195	95	74	84	52	500

presents the model’s confusion matrix under Scenario 1 (Baseline) for Farm A, summarizing the classification of each meteorological risk state over three months of validation data. The enhanced HMM achieved a macro-averaged accuracy of 89.2% and an F1-score of 0.86, surpassing both the Markov chain (82.5% accuracy) and naive Bayes (80.4% accuracy) baselines. Notably, the RNN performed similarly to the HMM in identifying wind-speed and temperature anomalies (F1-scores of around 0.85) but displayed higher variance across different validation intervals, possibly due to overfitting on a limited training dataset.

3.3.4. Severe Weather and Extreme Ocean Events

Under Scenario 2, which includes weeks with documented typhoon passages or storms above Beaufort scale 8, the HMM’s detection rate for “Extreme Storm Events” improved by approximately 10% relative to the Markov chain (Figure 5). Sensitivity tests showed that the real-time parameter adjustments—specifically, weighting newly observed storm data more heavily in the Baum–Welch updates—enhanced the timely recognition of intense gales. Despite occasional false positives when wave heights rose without concurrent high winds, the model maintained an F1-score near 0.80, highlighting robust detection even under swiftly escalating conditions.

3.3.5. Temperature Extremes and Cold Surges

For Scenario 3, which covers episodes of sea-air temperature differences surpassing ±8 °C from monthly norms, the model exhibited strong alignment between abnormal temperature observables and “Temperature Fluctuations” risk transitions.

Table 8. Summary statistics for cold surge events (Farm B, winter season).

Model	Cold Surge Events Detected	Total Events	Recall (%)	Avg. Lead Time (h)
Markov Chain	28	45	62.2	1.5
Naïve Bayes	25	45	55.6	1.0
RNN(LSTM)	34	45	75.6	2.5
HMM(Enhanced)	38	45	84.4	3.0

compiles summary statistics from the last winter season in Farm B, indicating that among 45 recorded cold-surge events, the model correctly predicted 38 (84.4% recall), with an average lead time of about 3 h before maintenance logs indicated mechanical stress or partial icing. In comparison, the naive Bayes baseline only correctly flagged 28 events (62.2% recall), underscoring the added value of dynamic state transitions and multi-scale temperature indicators.

3.3.6. Cross-Farm Generalization Test

In Scenario 4, we trained on four years of aggregated data from Farms A and B but validated on the final year from Farm C.

Table 9. Accuracy metrics for cross-farm generalization.

Training/Validation	Normal	Wind Speed	Ext. Storm	Ext. Ocean	Temp Fluc	F1-Macro
Farm C only (Train = Farm C, Test = Farm C)	0.90	0.78	0.83	0.75	0.80	0.82
Cross-Farm (Train = A + B, Test = C)	0.88	0.75	0.81	0.72	0.78	0.79

details the accuracy metrics for each of the four risk states, demonstrating that the enhanced HMM maintained an average F1-score of 0.79 across all categories, only marginally below the 0.82 it achieved when trained exclusively on Farm C’s historical data. Notably, wind speed–related events transferred more robustly than extreme ocean or temperature anomalies, likely reflecting the more universal nature of wind-shear patterns compared to localized cold-surge phenomena or seabed topographies that differ at Farm C.

Overall, these scenarios demonstrate that the enhanced HMM-based model surpasses the static Markov chain and naive Bayes approaches in terms of accuracy, recall, and overall robustness across variable meteorological conditions. Although the RNN competitor sometimes rivals or marginally outperforms the HMM in specific tasks (e.g., baseline wind-speed detection), it exhibits greater sensitivity to data scarcity and lacks the HMM’s interpretable structure of hidden states and dynamic transitions. In the subsequent sections (Section 3.4 and Section 3.5), we will delve deeper into the model’s adaptive parameter updates, computation times, and real-world implications—such as reduced downtime, improved safety margins, and cost savings through more precise risk forecasting.

This phase of experimentation sets the stage for a broader evaluation of how the HMM’s adaptive features and multi-scale risk definitions bolster offshore wind farm resilience under both everyday and extreme meteorological conditions.

3.4. Adaptive Parameter Updates and Computational Analysis

Having established the model’s core performance across various meteorological scenarios (Section 3.3), we next examine how adaptive parameter updates and computational efficiency affect both near-real-time and longer-term deployment in offshore wind operations. Unlike static approaches, the enhanced HMM is designed to recalibrate its transition and observation probabilities whenever new data reveal systematic deviations or emerging meteorological patterns.

As outlined in Section 3.2, we implemented two primary update mechanisms, as follows:

Periodic Retraining: Conducted weekly or monthly, integrating the latest dataset segment (e.g., 1–2 weeks of observations) into a Baum–Welch optimization routine.

Event-Triggered Refinement: Triggered by significant discrepancies—such as consistently underestimating gale-force winds in multiple consecutive forecast windows or failing to capture rising sea ice risks.

Figure 6 illustrates how frequently these updates were invoked in each experimental scenario. In the Baseline Scenario (Scenario 1), only minor adjustments (below 0.02 in absolute probability for key transitions) occurred every 2–3 weeks. In contrast, in the Severe Weather Scenario (Scenario 2), major storms prompted 2–4 adjustments per month, with transition probabilities for “Normal → Extreme Storm Events” climbing by as much as 0.05 during peak typhoon season. The Temperature Extremes Scenario (Scenario 3) exhibited moderate recalibrations, typically focused on refining the “Extreme Temperature” output probabilities in the late winter months.

To assess how these adaptive processes influence predictive quality, we measured changes in accuracy and recall immediately before and after an update cycle.

Table 10. Changes in accuracy/recall before and after updates.

Condition	State	Accuracy Before	Accuracy After	Recall Gain (%)
Storm 1	Extreme Storm	75%	85%	+10%
Storm 2	Extreme Storm	76%	86%	+10%
Cold Surge	Temp Fluc	62%	74%	+12%

shows the changes present before and after the updates.

Before major storm adjustments, the accuracy for detecting “Extreme Storm Events” dropped from approximately 86% to near 75% when a new storm pattern was not yet captured by the older probabilities. After re-optimization, accuracy rebounded above 85% within 1–2 days of newly observed data, indicating that weighting recent storm indicators more heavily in the transition matrix effectively captured the evolving risk dynamics. Similar trends were observed for Temperature Fluctuations, where large data corrections following abrupt cold surge episodes improved the model’s recall by around 12% (from 62% to 74%) in the winter season, as seen in Scenario 3 logs for Farm B. These findings reinforce the value of adaptive learning in maintaining robust forecasting, particularly in offshore environments prone to rapid weather swings. A key consideration for iterative or event-driven parameter tuning is computational overhead, which could potentially limit the applicability of the model in near-real-time operational environments. To assess this overhead, we measured the CPU time required for the following three principal update and inference methods:

Full Baum–Welch Recalibration: Involves processing up to 1–2 weeks of newly observed data, generally taking 20–40 s on a multi-core CPU, depending on the complexity (e.g., number of hidden states, size of observation matrices).

Incremental Updates: Utilizes an online approach that selectively refines specific parameters (e.g., states associated with “Extreme Storm Events”), typically requiring 5–10 s per update cycle.

Viterbi Decoding: Performed in under 1 s per inference step, often triggered every 10–15 min to maintain a rolling forecast and provide up-to-date risk assessments.

In Figure 7, we visualize the mean CPU times for these methods under four key scenarios across Farms A, B, and C: (1) baseline, (2) severe weather, (3) temperature extremes, and (4) cross-farm generalization. Each bar indicates the average runtime, with error bars depicting standard deviations from multiple runs. Notably, Scenario 2 (severe weather) and Scenario 3 (temperature extremes) exhibit moderate increases in compute time, driven by more frequent or substantial parameter shifts. However, the resultant precision gains help avert last-minute maintenance interventions or safety shutdowns, highlighting the trade-off between computational overhead and improved forecasting accuracy under volatile conditions.

While our adaptive HMM approach enhances responsiveness by recalibrating transition probabilities whenever new data significantly deviate from prior expectations, this flexibility also introduces non-trivial computational overhead. Specifically, full Baum–Welch retraining scales approximately linearly with both the length of the training window (number of observations) and the complexity of the HMM (number of states and observation symbols). Thus, doubling the data volume or increasing the state space can nearly double the runtime of a single retraining cycle. For incremental updates or event-triggered refinements—where only a subset of parameters is adjusted—the overhead grows more modestly, often enabling near-real-time operation even under frequent (hourly) recalibration. However, if the offshore wind farm’s SCADA system generates significantly denser data (e.g., 1 min sampling across many turbines), careful scheduling or batching of updates becomes essential to prevent the HMM from monopolizing computational resources. Our experiments show that, beyond a certain threshold (e.g., daily or semi-daily retraining), additional updates offer diminishing gains in detection accuracy while disproportionately increasing CPU usage. In practice, balancing a higher update frequency during volatile weather periods with less frequent recalibration under stable conditions allows operators to maintain robust forecasts while mitigating exponential growth in computational costs.

While frequent updates can enhance responsiveness, overly frequent recalibrations can risk model instability. Scenario 2 data showed that daily retraining on highly volatile storm inputs led to transient probability oscillations, inflating false positives for “Extreme Storm.” Enforcing a minimum 3-day update interval stabilized predictions for Scenario 2 and similarly benefited Scenario 3, ensuring the model adapts promptly yet consistently to evolving conditions.

Table 11. Comparison of re-training intervals (daily vs. weekly).

Retraining Interval	Detection Rate (%)	False Alarm Rate (%)	Accuracy (%)	F1-Score
Daily	82	10	83	0.80
Weekly	79	6	85	0.78

compares key performance indicators for different retraining intervals (daily vs. weekly) during Farm B’s winter period. A daily schedule increased extreme weather detection from 78% to 82% but also raised false alarms from 6% to 10%. In contrast, weekly updates slightly reduced detection (79%) but maintained false positives at 6%. These findings underscore the trade-off between maximum sensitivity to emerging patterns and the risk of overfitting to short-lived anomalies.

In evaluating the HMM’s performance against naive Bayes, a traditional Markov chain, and an LSTM-based RNN, we observed that the enhanced HMM generally excelled during rapidly changing conditions (such as sudden wind gusts, short-lived temperature extremes, and fast-onset storms). This superiority likely stemmed from the HMM’s ability to recalibrate its state transition probabilities in near-real-time, thereby capturing abrupt meteorological shifts more effectively than static or purely data-driven counterparts. In contrast, the naive Bayes model tends to overlook temporal transitions, causing it to under-detect high-risk states when wind or temperature fluctuates sharply. Likewise, the Markov chain, although time-aware, struggled with scenarios involving non-observable risk states and complex, multi-factor signals since it lacks the hidden-layer capability and adaptive refinements central to the HMM.

On the other hand, the LSTM-based RNN occasionally rivaled or surpassed the HMM in prolonged stable conditions, where data abundance enabled deep-learning models to learn extended temporal correlations. In these cases, the RNN’s memory mechanisms provided robust forecasts for gradual wind speed ramps or moderate temperature fluctuations. However, the RNN faced higher variance in performance, especially when encountering data-sparse intervals or infrequent but extreme events—issues that were partially mitigated in the HMM by its built-in probabilistic transitions and on-demand parameter updates. Consequently, while the HMM outperforms most baselines in volatile offshore meteorological contexts, the LSTM may improve when ample consistent data are available and sudden weather shifts are less frequent.

Overall, the results indicate that adaptive parameter updates significantly improve the model’s real-time tracking of rapid meteorological shifts, particularly for rare, high-impact conditions. Reasonable scheduling, supplemented by event triggers, provides a balance that stabilizes the HMM while capturing new weather regimes. From an operational standpoint, these iterative enhancements can prevent “blind spots”, ensuring that turbine controllers and maintenance teams stay informed about changing conditions without being overwhelmed by excessive false alarms.

With these insights on parameter tuning and computational costs, the forthcoming Section 3.5 will delve deeper into the practical implications for offshore wind farm management, including potential impacts on safety margins, maintenance strategies, and economic considerations associated with meteorological risk forecasting.

3.5. Practical Implications and Operational Insights

The experimental results and adaptive modeling strategies presented in the preceding sections provide several meaningful takeaways for the daily operation and strategic planning of offshore wind farms. By analyzing how meteorological risk states evolve and how the model responds to dynamic environmental inputs, farm operators and maintenance teams can benefit in the following ways:

The enhanced HMM framework demonstrated heightened sensitivity to emergent abnormal states, such as typhoons, rapid wind-shear escalations, and oceanic turbulence. Rapid updates to the transition probabilities, documented in Section 3.4, provided earlier alerts for high-impact events, affording operations personnel an additional 2–4 h of lead time compared to static models in some extreme weather scenarios. This advance warning can be critical in deciding whether to reduce turbine load, schedule emergency crew deployments, or implement other precautionary measures that mitigate downtime or damage.

Beyond mere forecasting, the model’s continuous refinement of risk states offers a data-driven basis for scheduling turbine inspections, planning spare part inventories, and mobilizing maintenance vessels. For instance, Scenario 3 (temperature extremes) indicated that anticipating cold-surge impacts up to several hours in advance allowed wind farms to minimize blade-icing risks through proactive heating or de-icing measures. As a result, farms reported a 10–15% decrease in maintenance-related downtime during peak winter months, suggesting direct operational cost savings linked to more precise and timely interventions.

By identifying and classifying risk states, the model also helps operators anticipate power output variability and plan accordingly. When the HMM flags an upcoming shift to “Wind Speed Fluctuations” or “Extreme Storm Events”, control systems can respond by adjusting pitch angles or balancing grid load demands in coordination with broader energy management systems. Over the study period, this proactive approach reduced unplanned turbine shutdowns by an estimated 8–12%, which is particularly beneficial in markets where grid stability and predictability command premium value.

The iterative updates shown in Section 3.4 underscore the feasibility of integrating the HMM model into existing SCADA (Supervisory Control and Data Acquisition) platforms. Real-time sensor feeds—especially for wave heights, wind gusts, and turbine load metrics—can be relayed to an embedded or cloud-based version of the model, ensuring up-to-date risk assessments. Although computational overhead rises with more frequent retraining or complex model extensions (e.g., hierarchical HMMs), the observed time of under 1–2 min per major re-calibration remains manageable for most operational cycles (5–15 min sampling).

In our HMM framework, each risk state is accompanied by a probability distribution (e.g., the likelihood of transitioning from “Normal” to “Extreme Storm Events” in the next time step). These state probabilities inherently capture uncertainty by indicating how confident the model is about a particular risk assessment. When the model’s probability mass is spread across multiple states (e.g., 40% for “Wind Speed Fluctuations”, 35% for “Temperature Fluctuations”, and 25% for “Extreme Ocean Events”), it implies higher uncertainty in the forecast. Operationally, we can further refine these uncertainties by tracking the variance of transition probabilities or by computing confidence intervals around key parameters (e.g., using a Bayesian approach or repeated EM runs with slightly perturbed data). For real-time decision-making, these probabilities—and, if desired, confidence metrics—are shared through the wind farm’s supervisory control interface. Operators thus see not only the most likely state but also a visualization of how split or concentrated the model’s belief is across states. Such probability profiles can be color-coded (e.g., green for high confidence in one state, orange if the model is split between two plausible states) or integrated into a warning tier system, ensuring that teams understand not just which risk is predicted but also facilitating more nuanced operational responses under ambiguous conditions.

The cross-farm generalization experiment (Section 3.3) highlighted the model’s adaptability to novel offshore contexts. While site-specific calibration remains important—especially for location-dependent factors like ocean currents and seafloor topographies—the model retained strong average accuracy and recall when transferred from Farms A/B to Farm C. This portability indicates that knowledge gleaned from historically monitored farms can guide the risk management strategies of newly commissioned or data-sparse sites.

Despite the encouraging performance, certain limitations merit attention. The model’s reliance on sufficiently dense real-time data presents challenges in regions with minimal instrumentation or frequent sensor outages. Additionally, while the multi-scale approach captures rapid and gradual transitions, extremely rare phenomena (e.g., once-in-decades super typhoons) may still be underrepresented. Future work could integrate Bayesian or ensemble techniques to better quantify uncertainty and explore deeper coupling with maintenance optimization algorithms that refine cost-effective interventions under uncertain wind and wave scenarios.

Collectively, these operational findings affirm that a dynamic HMM-based risk prediction model can enhance offshore wind farm reliability, lower operational costs, and mitigate safety hazards, particularly through early detection and adaptive responses to shifting meteorological threats. Subsequent sections (or the overall conclusion) may delve further into economic implications, broader system integration, and strategic deployment considerations, ensuring that the model’s advantages are effectively harnessed for real-world offshore wind power management.

3.6. Overall Observations, Limitations, and Future Directions

The preceding sections (Section 3.1, Section 3.2, Section 3.3, Section 3.4 and Section 3.5) outline a comprehensive experimental evaluation of the enhanced HMM-based meteorological risk prediction model in offshore wind power applications. Collectively, the results affirm the effectiveness of multi-farm data integration, adaptive parameter updates, and refined risk state definitions in boosting both predictive accuracy and operational usability. Notable highlights are as follows:

Consistent Gains Across Diverse Scenarios: Whether tested under baseline conditions or during severe meteorological events, the new approach demonstrated improved detection rates for high-risk states—such as extreme storms and critical temperature fluctuations—compared to static Markov or naive Bayes methods.

Adaptive Learning for Real-Time Responsiveness: By periodically recalibrating transition probabilities and giving extra weight to newly observed rare events, the model successfully addressed unforeseen extremes, limiting blind spots that might otherwise compromise turbine safety and power generation stability.

Practical Relevance and Cost-Effectiveness: Preliminary operational insights (Section 3.5) suggest tangible benefits for offshore wind farm operators, such as reduced downtime, earlier maintenance interventions, and optimized scheduling during peak risk intervals, potentially culminating in lower operational and repair expenses.

Despite these positive findings, several limitations and open questions merit attention. Firstly, the model’s performance hinges on adequate data coverage and sensor reliability, which can be challenging in remote offshore locations prone to communication gaps. Secondly, although the adaptive approach works well for moderate to frequent anomalies, it relies heavily on sufficient historical patterns—particularly for extremely rare or emerging phenomena such as once-in-decades super typhoons or seabed tectonic shifts. Addressing these data-scarce conditions may require supplementary Bayesian or ensemble-based strategies to quantify uncertainty more robustly. Additionally, the computational overhead of frequent retraining, while manageable in these experiments, could pose challenges at a massive scale or if more advanced hierarchical or deep-learning augmentations are introduced.

While this study emphasizes short- to medium-term meteorological forecasting for immediate operational needs, future expansions of the dynamic HMM approach could incorporate multi-year climate projections, either by coupling with long-range wind/climate models or by assimilating extended historical data. Such efforts would enable wind farm operators and planners to make more informed long-term decisions regarding turbine replacements, site expansions, and insurance and financing options. We believe this aligns with the reviewer’s suggestion and could yield broader insights into sustainable offshore wind development under evolving environmental conditions.

Moving forward, future research could exploit the following avenues:

Hierarchical HMMs: Incorporating multi-level structures to capture nuanced sub-states of storms (e.g., tropical depressions, typhoons) or granular equipment failure modes.

Hybrid Models with Reinforcement Learning or Neural Networks: Combining the interpretability of HMM states with the capacity for end-to-end optimization and deeper feature extraction.

Extended Deployment Trials: Evaluating the model continuously in operational wind farms over longer time horizons (e.g., multiple seasonal cycles) to assess real-world stability, maintenance cost reductions, and potential expansions to onshore or nearshore contexts.

Economic and Grid Integration Studies: Quantifying how improved meteorological risk forecasts might bolster grid stability, reduce balancing costs, or enhance power trading strategies in deregulated energy markets.

4. Conclusions

This research proposes a dynamic Hidden Markov Model (HMM) tailored to predict critical meteorological risks in offshore wind power, consistently achieving accuracy levels exceeding 70%—a notable improvement over static methods. By iteratively recalibrating the transition and emission probabilities with real-time data, the model effectively addresses the shortcomings of fixed-parameter approaches, enabling more reliable detection of extreme meteorological events, such as severe storms and rapid wind-speed fluctuations.

The proposed framework incorporates continuous data assimilation and iterative learning processes, allowing it to adjust swiftly to sudden environmental changes. Unlike conventional models that are reliant on extensive historical records or single-variable forecasting, this HMM-based solution updates risk assessments in near-real time, thus empowering wind farm operators to proactively modify turbine operations and maintenance schedules in anticipation of evolving weather hazards.

Capitalizing on multiple indicators, including wind speed, oceanic forces, and temperature anomalies, the model offers a holistic perspective on meteorological risk. This multi-dimensional focus captures the interplay between various physical phenomena, thereby surpassing simpler methods that often underperform in compound or hybrid risk scenarios. As such, the model more accurately reflects the complexities of offshore environments and supports targeted, data-driven decision-making.

Notwithstanding its demonstrated efficacy, the model still relies to some extent on expert knowledge for initial parameterization and may face data constraints under rare extreme conditions where historical records are sparse. Future advancements could incorporate ensemble-based or Bayesian HMMs to refine uncertainty quantification, expand observational scope through satellite remote sensing or buoy networks, and explore deep-learning-augmented or hierarchical model architectures for capturing nuanced spatiotemporal behaviors. Furthermore, coupling this HMM approach with optimization tools for scheduling and dispatch could establish a more comprehensive decision-support framework, strengthening both operational resilience and economic viability in offshore wind systems.

In addition, we see a promising avenue for integrating multi-criteria decision-making (MCDM) methods to guide operational strategies more effectively. Recent comparative hybrid MCDM studies [35,36]—for instance, those employing AHP-VIKOR or AHP-TOPSIS—demonstrate how systematically weighting diverse criteria can help select optimal solutions among multiple alternatives. In the context of offshore wind risk management, an HMM-based risk forecast could serve as one of the key inputs within an MCDM framework, allowing operators to balance conflicting objectives such as safety margins, maintenance costs, and energy output targets. This synergy would extend beyond merely identifying when meteorological hazards might occur; it would also provide a structured approach to choosing the best mitigation or allocation decisions under uncertain conditions.

Altogether, this dynamic HMM-based methodology delivers a robust, adaptive, and multi-indicator risk assessment paradigm for offshore wind power. By providing high predictive accuracy, rapid responsiveness to real-time data, and a holistic appraisal of meteorological hazards, the model surpasses conventional static counterparts and underpins strategic, proactive risk mitigation measures—advancing sustainability and operational security in the evolving landscape of renewable energy.