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Forecasting wind power - an ensemble technique with gradual coopetitive weighting based on weather situation

机译：风力发电量预测-一种基于天气情况的逐步竞争加权的集成技术

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The prediction of the power generation of wind farms is a non-trivial problem with increasing importance during the last decade due to the rapid increase of wind power generation in the power grid. The prediction task is commonly addressed using numerical weather predictions, statistical methods, or machine learning techniques. Various articles have shown that ensemble techniques for forecasting can yield better results regarding forecasting accuracy than single techniques alone. Typical ensembles make use of a parameter, or data diversity approach to build the models. In this article, we propose a novel ensemble technique using both, cooperative and competitive characteristics of ensembles to gradually adjust the influences of single forecasting algorithms in the ensemble based on their individual strengths using a “coopetitive” weighting formula. The observed quality of the models during training is used to adaptively weigh the models based on the location in the input data space (i.e., depending on the weather situation). We compute the overall weights for a particular weather situation using both, a spatial as well as a global weighting term. The experimental evaluation is performed on a data set consisting of data from 45 wind farms, which is made publicly available. We demonstrate that the technique is among the best performing algorithms compared to other state-of-the-art algorithms and ensembles. Furthermore, the practical applicability of the proposed technique is discussed.
机译：风电场发电量的预测是一个重要的问题，由于电网中风能发电量的快速增长，在过去十年中，风电场的发电量越来越重要。通常使用数值天气预报，统计方法或机器学习技术来解决预测任务。各种文章已经表明，与单独的单个技术相比，用于预测的集成技术可以产生更好的预测准确性结果。典型的合奏使用参数或数据分集方法来构建模型。在本文中，我们提出了一种新颖的集成技术，该方法利用集成的协作和竞争特征，根据单个预测算法的个体优势，使用“竞争性”加权公式逐渐调整单个预测算法在集成中的影响。训练期间观察到的模型质量用于根据输入数据空间中的位置（即取决于天气情况）对模型进行自适应加权。我们使用空间权重和全局权重项来计算特定天气情况的总权重。实验评估是在包含45个风电场数据的数据集上进行的，该数据集已公开提供。我们证明，与其他最新算法和集成算法相比，该技术是性能最佳的算法之一。此外，讨论了所提出的技术的实际适用性。

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