SDWPF dataset

Wind power, while a clean and safe form of renewable energy, presents integration challenges to the grid system due to its variable supply. Consequently, Wind Power Forecasting (WPF) is pivotal for the successful integration and operation of wind energy. Existing publicly available WPF datasets, however, often feature a limited number of wind turbines and typically lack granular details on turbine locations and dynamic contextual information. To address this gap and promote the advancements of WPF research, we introduce a dataset called the Spatial Dynamic Wind Power Forecasting dataset (SDWPF). This dataset comprises data collected over one year from a wind farm housing an array of 134 wind turbines. The SDWPF dataset includes the spatial distribution of the wind turbines and the dynamic context factors for each turbine. Further leveraging the potential of SDWPF, we initiated the ACM KDD Cup 2022, drawing registrations from over 2400 teams globally.

风电作为一种清洁安全的可再生能源，但其出力具有波动性，会给电网系统的并网运行带来诸多挑战。因此，风电功率预测（Wind Power Forecasting，WPF）对于风电的高效并网与稳定运行至关重要。然而，当前已公开的风电功率预测数据集往往仅包含少量风力发电机组，且普遍缺乏机组位置的精细化细节与动态上下文信息。为填补这一研究空白、推动风电功率预测领域的研究进展，我们推出了空间动态风电功率预测数据集（Spatial Dynamic Wind Power Forecasting Dataset，SDWPF）。该数据集采集自一座部署有134台风力发电机组的风电场，数据采集周期长达一年。该数据集涵盖了风电机组的空间分布信息，以及每台机组对应的动态上下文因素。依托该数据集的应用潜力，我们发起了ACM KDD杯2022赛事，最终吸引了全球超过2400支队伍报名参赛。

搜集汇总

背景与挑战

背景概述

SDWPF dataset是一个包含134台风力涡轮机一年数据的风力发电预测数据集，涵盖涡轮机空间分布和动态上下文信息，用于支持风力发电预测研究。