A Residual Learning Framework for ERA5 Offshore Wind Speed Bias Correction and Gap Reconstruction Toward Offshore Wind Energy Applications

Continuous and reliable offshore wind speed time series are essential for offshore wind resource assessment, power forecasting, and long-term wind energy analysis. However, ERA5 reanalysis data may contain site-scale biases over offshore areas, and missing records in offshore wind-tower observations limit their practical use. To improve the reliability of offshore wind speed series, this study proposes a residual learning framework for ERA5 bias correction and continuous gap reconstruction by integrating ERA5 background fields with target- and neighboring-station information. Using FINO1 and FINO3 observations in the North Sea from 2018 to 2025, ERA5 wind-vector components were matched with tower observations, and residuals of the east–west and north–south wind components were used as prediction targets. Statistical methods, linear models, tree-based ensemble models, and GRU models were evaluated under a cross-year framework, with additional analyses of historical residuals, neighboring-station information, and wind-direction-related features. Results show that target-station historical residuals dominate site-scale correction, reducing RMSE by 50.77% and 32.69% for FINO1 and FINO3, respectively, under a 1 h observation lag. Neighboring-station information provides useful constraints when local observations are limited or missing. The framework maintains stable improvements across years and strong-wind conditions and supports reliable offshore wind speed reconstruction for wind energy applications.