用连续级联模式作表层风速的短期预报
Short term forecasting of surface layer wind speed using a continuous cascade model
摘要：
本文介绍了一种基于连续级联概念的表层风速幅值短期预报的统计方法。最近的经验研究结果表明，在中尺度范围内存在一些级联过程的启发，我们认为，风速可以描述的季节性成分和波动的一部分表示的“多重分形噪声”与随机级联。我们的模型的性能进行了测试，每小时风速系列收集在科西嘉岛（法国）和荷兰的各个地点。所获得的结果显示了系统的改进的预测相比，参考模型，如持久性或人工神经网络。
英文摘要：
This paper describes a statistical method for short-term forecasting of surface layer wind velocity amplitude relying on the notion of continuous cascades. Inspired by recent empirical findings that suggest the existence of some cascading process in the mesoscale range, we consider that wind speed can be described by a seasonal component and a fluctuating part represented by a "multifractal noise" associated with a random cascade. Performances of our model are tested on hourly wind speed series gathered at various locations in Corsica (France) and Netherlands. The obtained results show a systematic improvement of the prediction as compared to reference models like persistence or Artificial Neural Networks.
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