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Accurate wave height measurements are critical for offshore wind farm operations, marine navigation, and environmental monitoring. Wave buoys provide essential real-time data; however, their reliability is compromised by harsh marine conditions, resulting in frequent data gaps due to sensor failures, maintenance issues, or extreme weather events. These disruptions pose significant risks for decision-making in offshore logistics and safety planning. While numerical wave models and machine learning techniques have been explored for wave height prediction, most approaches rely heavily on historical data from the same buoy, limiting their applicability when the target sensor is offline. This study addresses these limitations by developing a virtual wave buoy model using a network-based data-driven approach with Random Forest Regression (RFR). By leveraging wave height measurements from surrounding buoys, the proposed model ensures continuous wave height estimation even in the case of malfunctioning physical sensors. The methodology is tested across four offshore sites, including operational wind farms, evaluating the sensitivity of predictions to buoy placement and feature selection. The model demonstrates high accuracy and incorporates a k-nearest neighbors (kNN) imputation strategy to mitigate data loss. These findings establish RFR as a scalable and computationally efficient alternative for virtual sensing, thereby enhancing offshore wind farm resilience, marine safety, and operational efficiency. 

In this paper, we suggest a framework for determining the best operation and maintenance strategies for offshore wind turbines. The framework takes into account both quantitative and qualitative data gathered from the wind turbines. The proposed framework consists of a simulation‐optimization approach for designing, planning, and scheduling maintenance operations for offshore wind farms and finding the optimal intervention solution for minimizing costs while keeping a high availability of wind turbines and guaranteeing safety standards for workers. Several parameters and constraints are addressed to account for the realistic complexity of the problem, such as weather conditions, resource cost, and maintenance duration. A numerical case study focusing on offshore wind turbine blade maintenance is presented to demonstrate the implementation of the proposed framework. The example simulates realistic defect progression scenarios, stratified by severity level, and incorporates empirically grounded estimates of failure rates, repair costs, technician requirements, and vessel logistics. The study illustrates how the simulation‐optimization approach integrates economic considerations, resource constraints, and safety risk factors to support data‐informed maintenance scheduling decisions under uncertainty.