Due to the stochastic and non-stationary characteristics of wind speed, the wind power generation is highly uncertain and fluctuating, which significantly challenges the operation of the power system and the associated electricity market. In this article, a new spatial-temporal method is proposed for short-term wind power prediction based on image inputs and augmented convolutional network. First, the geographical locations of various wind farms and the relevant wind vectors are processed into a series of multiframe spatial-temporal wind images, which can be handled by the convolutional networks. Then, wind power conversion and prediction models are developed based on those networks, where recurrent paths and attention mechanism are introduced to enhance the model architecture. The testing results have validated the high performance of the proposed method within a forecast horizon of up to seven hours. In particular, even when the terrain information is not available, the implicit wind flow field within the original inputs can still be approximately learned by the proposed convolutional networks.

中文翻译：

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用于风电预测的增强卷积网络：具有时空图像输入的新循环架构设计

由于风速的随机性和非平稳性，风力发电具有高度的不确定性和波动性，对电力系统和相关电力市场的运行提出了重大挑战。在本文中，提出了一种新的基于图像输入和增强卷积网络的短期风功率预测时空方法。首先，将各个风电场的地理位置和相关的风向量处理成一系列多帧时空风图像，这些图像可以由卷积网络处理。然后，基于这些网络开发风电转换和预测模型，其中引入循环路径和注意机制来增强模型架构。测试结果验证了所提出方法在长达 7 小时的预测范围内的高性能。特别是，即使地形信息不可用，原始输入中的隐式风流场仍然可以通过所提出的卷积网络近似学习。