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Sub-hourly forecasting of wind speed and wind energy

机译：亚小时风速和风能预报

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The need to have access to accurate short term forecasts is essential in order to anticipate the energy production from intermittent renewable sources, notably wind energy. For hourly and sub-hourly forecasts, benchmarks are based on statistical approaches such as time series based methods or neural networks, which are always tested against persistence. Here we discuss the performances of downscaling approaches using information from Numerical Weather Prediction (NWP) models, rarely used at those time scales, and compare them with the statistical approaches for the wind speed forecasting at hub height. The aim is to determine the added value of Model Output Statistics for sub-hourly forecasts of wind speed, compared to the classical time series based methods. Two downscaling approaches are tested: one using explanatory variables from NWP model outputs only and another which additionally includes local wind speed measurements. Results of both approaches and of the classical time series based methods, tested against persistence on a specific wind farm, are considered. For both hourly and sub-hourly forecasts, adding explanatory variables derived from observations in the downscaling models gives higher improvements over persistence than the benchmark methods and than the downscaling models using only the NWP model outputs. (C) 2019 Elsevier Ltd. All rights reserved.
机译：为了预测间歇性可再生能源（特别是风能）的能源生产，必须具有准确的短期预测。对于每小时和不足一个小时的预测，基准是基于统计方法（例如基于时间序列的方法或神经网络）进行的，这些方法总是针对持久性进行测试。在这里，我们使用数值天气预报（NWP）模型中的信息（在这些时间尺度上很少使用）讨论降尺度方法的性能，并将它们与轮毂高度风速预测的统计方法进行比较。与传统的基于时间序列的方法相比，其目的是确定模型输出统计量的附加值，以进行亚小时风速预测。测试了两种缩减规模的方法：一种仅使用NWP模型输出的解释变量，另一种还包括本地风速测量。考虑了针对特定风电场的持续性进行测试的两种方法的结果以及基于经典时间序列的方法的结果。对于小时和不足小时的预测，在降尺度模型中添加从观测值得出的解释变量比基准方法和仅使用NWP模型输出的降尺度模型在持久性方面有更高的改进。 （C）2019 Elsevier Ltd.保留所有权利。

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