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Characterized by sudden changes in strength, complex influencing factors, and significant impacts, the wind speed in the circum-Bohai Sea area is relatively challenging to forecast. On the western side of Bohai Bay, as the economic center of the circum-Bohai Sea, Tianjin exhibits a high demand for accurate wind forecasting. In this study, three machine learning algorithms were employed and compared as post-processing methods to correct wind speed forecasts by the Weather Research and Forecast (WRF) model for Tianjin. The results showed that the random forest (RF) achieved better performance in improving the forecasts because it substantially reduced the model bias at a lower computing cost, while the support vector machine (SVM) performed slightly worse (especially for stronger winds), but it required an approximately 15 times longer computing time. The back propagation (BP) neural network …