One of the critical challenges in wind energy development is the uncertainty quantification. Prior knowledge about the wind speed in look-ahead times in shape of probabilistic information plays a pivotal role in the optimal operation and planning in the electrical networks. In this article, we design a deep learning-based approach to characterize the probability density function (PDF) of the wind for the next hours. The proposed method is directly applicable to raw data and directly constructs PDFs and enhances the level of accuracy and reliability as well as computational efficiency. Furthermore, we utilize the convolutional neural network to enhance learning spatial features. To provide a better understanding of temporal features, a recurrent neural network, called gated recurrent unit, is utilized. To directly construct PDFs, a gradient-based loss function is proposed, and the training procedure is modified. The effectiveness and superiority of the proposed probabilistic wind speed forecasting are verified by two actual datasets, i.e., London, England, and Shiraz, Iran, and comprehensive numerical results validate the performance of the proposed approach in comparison with several state-of-the-art and previously investigated approaches in terms of sharpness, accuracy, and reliability.

中文翻译：

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先进的深度学习方法用于概率风速预测

风能发展中的关键挑战之一是不确定性量化。有关概率信息形状的提前了解风速的先验知识在电网的最佳运行和计划中起着关键作用。在本文中，我们设计了一种基于深度学习的方法来表征接下来几小时的风的概率密度函数（PDF）。所提出的方法直接适用于原始数据，直接构建PDF，提高了准确性，可靠性和计算效率。此外，我们利用卷积神经网络来增强学习空间特征。为了更好地理解时间特征，使用了称为门控递归单元的递归神经网络。要直接构建PDF，提出了一种基于梯度的损失函数，并对训练过程进行了修改。通过两个实际的数据集，即英国伦敦和伊朗设拉子，验证了所提出的概率风速预报的有效性和优越性，综合的数值结果与几种最新状态相比，验证了所提出的方法的性能。技术和先前研究的方法的清晰度，准确性和可靠性。