Modeling wind speed has a significant impact on wind energy systems and has attracted attention from numerous researchers. The prediction of wind speed is considered a challenging task because of its natural nonlinear and random characteristics. Therefore, machine learning models have gained popularity in this field. In this paper, three machine learning approaches – Gaussian process regression (GPR), bagged regression trees (BTs) and support vector regression (SVR) – were applied for prediction of the weekly wind speed (maximum, mean, minimum) of the target station using other stations, which were specified as reference stations. Daily wind speed data, gathered via the Malaysian Meteorological Department at 14 measuring stations in Malaysia covering the period between 2000 and 2019, were used. The results showed that the average weekly wind speed had superior performance to the maximum and minimum wind speed prediction. In general, the GPR model could effectively predict the weekly wind speed of the target station using the measured data of other stations. Errors found in this model were within acceptable limits. The findings of this model were compared with the measured data, and only Kota Kinabalu station showed an unacceptable range of prediction. To investigate the prediction performance of the proposed model, two models were used as the comparison models: the BTs model and SVR model. Although the comparison of GPR with the BTs model at Kuching station showed slightly better performance for the BTs model in maximum and minimum wind speed prediction, the prediction outcomes of the other 13 stations showed better performance for the proposed GPR model. Moreover, the proposed model generated smaller prediction errors than the SVR model at all stations.

风速建模对风能系统具有重大影响，并引起了众多研究人员的关注。由于风速的自然非线性和随机特性，风速预测被认为是一项具有挑战性的任务。因此，机器学习模型在该领域获得了普及。在本文中，三种机器学习方法——高斯过程回归（GPR）、袋装回归树（BTs）和支持向量回归（SVR）——被应用于预测目标站的每周风速（最大值、平均值、最小值）使用指定为参考站的其他站。使用了通过马来西亚气象局在马来西亚 14 个测量站收集的 2000 年至 2019 年期间的每日风速数据。结果表明，周平均风速预测优于最大和最小风速预测。总的来说，GPR模型可以利用其他站点的实测数据有效地预测目标站点的周风速。在此模型中发现的错误在可接受的范围内。将该模型的结果与实测数据进行了比较，只有哥打京那巴鲁站显示出不可接受的预测范围。为了研究所提出模型的预测性能，使用了两个模型作为比较模型：BTs 模型和 SVR 模型。虽然 GPR 与古晋站 BTs 模型的比较显示，BTs 模型在最大和最小风速预测方面的性能略好，其他 13 个台站的预测结果表明提出的 GPR 模型具有更好的性能。此外，所提出的模型在所有站点上产生的预测误差都比 SVR 模型小。