Data‐driven long‐term wind speed forecasting and techno‐economics of offshore wind‐to‐hydrogen production

Abstract The economic viability of offshore wind‐to‐hydrogen systems depends heavily on the accuracy of wind‐speed forecasting. We evaluated three probabilistic scenario generators (historical bootstrapping, parametric Weibull fitting, and calibrated long short‐term memory (LSTM) sequence model) using a decision‐coupled framework. From 61 years of ERA5, we created 1000 synthetic 23‐year hourly scenarios per method, propagated them through a techno‐economic model, and scored the continuous ranked probability score (CRPS) on economic distributions (levelized cost of hydrogen (LCOH), net present value (NPV), and interest rate of return (IRR)). We report the decision bandwidth and its elasticity to skill, and run a financing price sweep to meet probability targets. Relative to bootstrapping and Weibull, the LSTM lowers CRPS by 30% for LCOH and NPV (25% for IRR), narrowing risk bands at similar medians. The hydrogen price required to reach more than 90% probability is €7.76–7.78/kg for Pr ( NPV > 0) and €9.16‐9.18/kg for Pr ( NPV > 0 and IRR > 10%). A cross‐method spread of less than €0.02/kg indicates a threshold‐saturated regime, in which better skills mainly contract risk. Robustness tests shift medians, but preserve method ranking and thresholds. This framework translates probabilistic forecasting skills into finance ability metrics for wind‐to‐hydrogen screening and policy design.