Integrated decision support tools for managing operations and maintenance of offshore wind farms on different time scales

Decision support tools are vital for optimizing operations and maintenance planning in offshore wind energy. This study addresses two distinct planning horizons for the North Sea region: short-term operational planning with typical lead times of up tothree days, and long-term strategicplanning extendingbeyond standard forecasting periods. The long-term framework emphasizes analyzing extreme wind and wave events by assessing their magnitudes and return period probabilities, utilizing decades-long, high-resolution numerical simulations at hourly intervals. Data integration involves outputs from the regional atmospheric model COSMO-CLM and the wave model WAM. Furthermore, optimal weather windows for infrastructure deployment are determined by evaluating probabilities of favorable meteorological and oceanographic conditions across different time frames. For short-term forecasting, we propose a machine learning-based stochastic weather generator . This tool employs Long Short-Term Memory neural networks . This approach generates synthetic met-ocean time series, providing accurate short-term predictions of wind speed and wave height. By combining these methodologies, the study provides actionable insights to enhance resource utilization and streamline offshoreinfrastructure deployment. • Decision-making tools are introduced for wind energy management. • Probabilistic and deterministic approaches support long- and short-term planning. • A dual-scale method supports offshore operations from synoptic to annual scales. • Long-term weather window statistics use 74 years of wind and wave simulations. • BI-LSTM networks provide accurate 3-day forecasts for site-specific met-ocean data.