Improving multi-modal wind speed prediction of short and medium term with a bi-clustered machine learning method

Abstract. Accurate prediction of wind speed is of great importance for stable and reliable operation of wind farms. However, the single numerical model forecast cannot provide precise wind speed outputs due to the defect of its physical parameterization scheme, whose error will gradually grow with increasing prediction time. Therefore, we proposed a model named Bi-clustered Recursive Bayesian Forest (BCRBR) for wind speed prediction and correction. The approach incorporated Sea-land Breeze and weather stability effects, integrating an atmospheric circulation index as input features; wind farm data underwent modal classification via bi-clustering to mitigate wind speed magnitude interactions, followed by machine learning-based correction of wind speed. The method was proved to be effective for wind speed prediction correction. Compared to forecasts from the Weather Research and Forecasting model, wind speed error indicators were reduced by more than 60 %; and the forecast precision increased from 30.2 % to 78.4 %, of which the improvement is more than twice. Compared to other models, the proposed model presented favorable correction results in different types of wind field, indicating its greater versatility and stronger competitiveness than other models.