Wind speed forecasting can serve a wide spectrum of purposes, including scheduling of a power system and dynamic control of structures. A lot of models are widely used to forecast wind speed, consisting of deterministic models (e.g., physical models, statistical models, and artificial intelligence models) and probabilistic models (e.g., Bayesian model). The wind speed has the characteristics of random, nonlinear, and uncertainty, which highlights the importance of using Bayesian model to predict the wind speed. In this study, a Bayesian emulator with Gaussian process prior is adopted for probabilistic forecast of wind speed. The present Bayesian emulator approach not only maintains the data‐driven property which guarantees its high flexibility in modeling the complexity of the target system but also allows for the efficient, probabilistic evaluation of the wind speed in terms of the predictive mean and variance. Nevertheless, the modeling performance of the Bayesian emulator directly depends on the selected covariance function. The influence of different types of covariance functions, which include squared‐exponential (SE) covariance function, Matern (MA) covariance function, periodic (PE) covariance function, and composite covariance function, on forecasting performance of the wind speed is studied. One‐month wind monitoring data collected by structural health monitoring (SHM) system installed on Jiubao bridge are employed to demonstrate the effectiveness of Bayesian emulator for forecasting the wind speed.

中文翻译：

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基于贝叶斯仿真器的监测数据风速概率预测

风速预测可用于多种用途，包括电力系统的调度和结构的动态控制。许多模型广泛用于预测风速，包括确定性模型（例如物理模型，统计模型和人工智能模型）和概率模型（例如贝叶斯模型）。风速具有随机，非线性和不确定性的特征，这突出了使用贝叶斯模型预测风速的重要性。在这项研究中，采用具有高斯过程先验的贝叶斯仿真器来预测风速。当前的贝叶斯仿真器方法不仅保留了数据驱动的属性，从而保证了其在对目标系统的复杂性进行建模方面的高度灵活性，而且还实现了高效，根据预测均值和方差对风速进行概率评估。尽管如此，贝叶斯仿真器的建模性能直接取决于所选的协方差函数。研究了不同类型的协方差函数，包括平方指数（SE）协方差函数，Matern（MA）协方差函数，周期（PE）协方差函数和复合协方差函数，对风速预报性能的影响。通过安装在九堡大桥上的结构健康监测（SHM）系统收集的一个月风监测数据，证明了贝叶斯仿真器预测风速的有效性。贝叶斯仿真器的建模性能直接取决于所选的协方差函数。研究了平方指数（SE）协方差函数，Matern（MA）协方差函数，周期（PE）协方差函数和复合协方差函数等不同类型的协方差函数对风速预报性能的影响。通过安装在九堡大桥上的结构健康监测（SHM）系统收集的一个月风监测数据，证明了贝叶斯仿真器预测风速的有效性。贝叶斯仿真器的建模性能直接取决于所选的协方差函数。研究了不同类型的协方差函数，包括平方指数（SE）协方差函数，Matern（MA）协方差函数，周期（PE）协方差函数和复合协方差函数，对风速预报性能的影响。通过安装在九堡大桥上的结构健康监测（SHM）系统收集的一个月风监测数据，证明了贝叶斯仿真器预测风速的有效性。对风速预报性能进行了研究。通过安装在九堡大桥上的结构健康监测（SHM）系统收集的一个月风监测数据，证明了贝叶斯仿真器预测风速的有效性。对风速预报性能进行了研究。通过安装在九堡大桥上的结构健康监测（SHM）系统收集的一个月风监测数据，证明了贝叶斯仿真器预测风速的有效性。