The fluctuation of the wind speed and direction is due to the stochastic nature of the wind and the enforcing atmospheric pressure. Accordingly, the output power forecasting of the wind farms (WFs) will be difficult. In this paper, a novel method based on Gaussian Processes (GPs) is proposed to improve the probabilistic prediction of the WF levels and regional WFs. The Covariance Functions (CFs) are the key ingredient in using GPs. Thus, different groupings of CFs are investigated comprehensively. The GP includes different types as dynamic, static, direct, indirect, and combined structures, which are investigated in this study. The results of comparison between dynamic and static GP, reveal that the dynamic GP generates keen Prediction Intervals (PIs). In addition, with comparing the accuracy of direct and indirect prediction plan, it shows that indirect prediction strategy brings about wider PIs. The various evaluation metrics have applied to benchmark the different methods performance, and its results show that the indirect–dynamic GP has better performance than other combined structures of GP as well as other methods, in both WF levels and regional WFs, while its maximum error has obtained as about 5% less than others. Moreover, the proposed model provides precise results of forecasted energy in every time steps in both deterministic and probabilistic wind power forecasting. The compared results between indirect–dynamic GP and other structures show the highest average coverage error, about 1% and 2.2% higher in the regional level and WF levels, respectively, the lowest prediction interval nominalized average width, about 5% and 15% lower in the regional level and WF levels, respectively, and the highest interval sharpness, about 2% and 5% higher in the regional level and WF levels, respectively.

风速和风向的波动是由于风的随机性和强制性大气压造成的。因此，风电场（WFs）的输出功率预测将很困难。本文提出了一种基于高斯过程（GPs）的新方法，以提高对WF水平和区域WF的概率预测。协方差函数（CF）是使用GP的关键要素。因此，对CF的不同分组进行了全面研究。GP包括动态结构，静态结构，直接结构，间接结构和组合结构等不同类型，本研究对此进行了研究。动态和静态GP的比较结果表明，动态GP生成敏锐的预测间隔（PI）。此外，通过比较直接和间接预测计划的准确性，它表明间接预测策略带来了更大的效绩指标。各种评估指标已用于对不同方法的性能进行基准测试，其结果表明，间接-动态GP在WF水平和区域WF方面均优于GP的其他组合结构以及其他方法，但其最大误差是比其他人少获得约5％。此外，在确定性和概率性风电功率预测中，所提出的模型在每个时间步中都提供了精确的预测能量结果。间接动力GP与其他结构之间的比较结果显示，平均覆盖误差最高，区域水平和WF水平分别高约1％和2.2％，最低预测区间标称平均宽度，