论文标题
SDWPF：KDD CUP 2022的空间动态风能预测挑战的数据集
SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022
论文作者
论文摘要
风能供应的可变性可能会给将风能纳入网格系统带来重大挑战。因此，风力预测（WPF）已被广泛认为是风能整合和操作中最关键的问题之一。在过去的几十年中，关于风能预测问题的研究爆炸了。然而，如何很好地处理WPF问题仍然具有挑战性，因为始终要求高预测准确性以确保电网稳定性和供应的安全性。我们提出了独特的空间动态风能预测数据集：SDWPF，其中包括风力涡轮机的空间分布以及动态上下文因素。鉴于，大多数现有数据集只有少量的风力涡轮机，而无需以细粒度的时间尺度了解风力涡轮机的位置和上下文信息。相比之下，SDWPF提供了半年多的风力涡轮机的风能数据，其相对位置和内部地位。我们使用此数据集启动BAIDU KDD杯2022来检查当前WPF解决方案的极限。该数据集在https://aistudio.baidu.com/aistudio/competition/detail/152/0/datasets上发布。
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.
