Accurate prediction of wind speed at sea is crucial for the site selection of wind farms, the layout of wind turbines, and the estimation of power generation. To improve the accuracy of short-term predictions under limited data conditions, this study proposes a backpropagation (BP) neural network prediction model optimized by the particle swarm optimization algorithm (PSO). This model is trained using hourly wind speed data from meteorological stations along the northeastern coast of China from 2020 to 2022, and two modeling strategies, namely the unified training model over multiple years and the seasonal model, are constructed for comparison. The validation using the measured data from January to July 2023 indicates that the unified model with a root mean square error of 1.235 and an average absolute error of 0.924 demonstrates superior generalization performance, outperforming the seasonal models (such as the spring model with RMSE = 1.243 and the summer model with RMSE = 1.324). Benchmark comparisons against LSTM, ARIMA, and persistence models further confirmed the superiority of the proposed approach. To address the stochastic nature of wind speed and support grid operation, we extended the deterministic forecasts to probabilistic prediction intervals using Monte Carlo Dropout, achieving a prediction interval coverage probability of 81.2% with a mean width of 1.38 m/s. The results indicate that while seasonal modeling offers insights into intra-annual wind variations, it does not exceed the accuracy of the globally trained multi-year model under limited data conditions. In conclusion, the proposed BP-PSO hybrid model provides a robust and low-cost solution for offshore wind speed forecasting, with the probabilistic forecasting framework offering actionable uncertainty information for grid integration. The multi-year training framework demonstrates stronger practical utility, and the findings support the application of hybrid optimization algorithms in real-world wind resource assessment.
Zhang et al. (Tue,) studied this question.