Wind Speed Modeling by Nested ARIMA Processes
Wind Speed Modeling by Nested ARIMA Processes
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DOI：
10.3390/en12010069
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发表时间：
2018-12
期刊：
影响因子：
3.2
通讯作者：
Sokhom Sim;P. Maass;P. Lind
中科院分区：
文献类型：
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作者：
Sokhom Sim;P. Maass;P. Lind
风速模型在基础研究和应用方面都日益引起人们的兴趣，例如风力涡轮机的开发和建造大型风力发电厂的战略。一般来说，这种建模受到风速数据的非平稳特征的阻碍，这些特征在很大程度上反映了大气中的湍流动力学。我们研究如何通过嵌套的ARIMA模型捕获这些特征。在这种方法中，风速在给定时间窗内的波动由一个随机过程来模拟，而参数在连续时间窗之间的变化由另一个随机过程来模拟。为了推导风速模型，我们使用了在北海FINO1平台收集的20个月的数据，并使用了一个变量转换，该变量转换最好地将风速映射到高斯随机变量上。我们发现风速增量可以很好地再现多达四个标准差。然而，极端变化的分布与模型预测大相径庭。
Wind speed modelling is of increasing interest, both for basic research and for applications, as, e.g., for wind turbine development and strategies to construct large wind power plants. Generally, such modelling is hampered by the non-stationary features of wind speed data that, to a large extent, reflect the turbulent dynamics in the atmosphere. We study how these features can be captured by nested ARIMA models. In this approach, wind speed fluctuations in given time windows are modelled by one stochastic process, and the parameter variation between successive windows by another one. For deriving the wind speed model, we use 20 months of data collected at the FINO1 platform at the North Sea and use a variable transformation that best maps the wind speed onto a Gaussian random variable. We find that wind speed increments can be well reproduced for up to four standard deviations. The distributions of extreme variations, however, strongly deviate from the model predictions.