标题： SDWPF：2022年KDD杯空间动态风功率预测挑战赛数据集 显示英文标题

标题： SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022

摘要： 风力发电供应的波动性可能给将风力发电并入电网系统带来重大挑战。 因此，风电功率预测（WPF）已被广泛认为是风力发电整合与运行中最关键的问题之一。 在过去的几十年里，关于风电功率预测问题的研究呈现爆炸式增长。 然而，如何很好地处理WPF问题仍然具有挑战性，因为始终需要高预测精度以确保电网稳定性和供电安全性。 我们提出了一个独特的空间动态风电功率预测数据集：SDWPF，其中包含了风力涡轮机的空间分布以及动态上下文因素。 然而，大多数现有的数据集只有少量风力涡轮机，并且不知道风力涡轮机在细粒度时间尺度上的位置和上下文信息。 相比之下，SDWPF提供了来自风场的134台风力涡轮机在半年内的风电功率数据及其相对位置和内部状态。 我们使用该数据集来举办百度KDD杯2022，以检验当前WPF解决方案的极限。 数据集已发布在https://aistudio.baidu.com/aistudio/competition/detail/152/0/datasets。 显示英文摘要

摘要： The variability of wind power supply can present substantial challenges to incorporating wind power into a grid system. Thus, Wind Power Forecasting (WPF) has been widely recognized as one of the most critical issues in wind power integration and operation. There has been an explosion of studies on wind power forecasting problems in the past decades. Nevertheless, how to well handle the WPF problem is still challenging, since high prediction accuracy is always demanded to ensure grid stability and security of supply. We present a unique Spatial Dynamic Wind Power Forecasting dataset: SDWPF, which includes the spatial distribution of wind turbines, as well as the dynamic context factors. Whereas, most of the existing datasets have only a small number of wind turbines without knowing the locations and context information of wind turbines at a fine-grained time scale. By contrast, SDWPF provides the wind power data of 134 wind turbines from a wind farm over half a year with their relative positions and internal statuses. We use this dataset to launch the Baidu KDD Cup 2022 to examine the limit of current WPF solutions. The dataset is released at https://aistudio.baidu.com/aistudio/competition/detail/152/0/datasets.