SDWPF:在大型轮机阵列上进行空间动态风电预测的数据集.
Jingbo Zhou1, Xinjiang Lu2, Yixiong Xiao2
1Business Intelligence Lab, Baidu Research, Beijing, China. zhoujingbo@baidu.com.
Scientific data
|June 19, 2024
概括
一个新的空间动态风力发电预测 (SDWPF) 数据集通过包括轮机空间数据和动态因素来增强风力发电集成. 这有助于推进可再生能源电网管理.
科学领域:
- 可再生能源系统可再生能源系统
- 数据科学数据科学数据科学
- 电网整合 电网整合
背景情况:
- 风力发电是一种清洁的可再生能源,由于其固有的变化性,它面临着电网整合的挑战.
- 精确的风力发电预测 (WPF) 对于管理电网稳定性和最大限度地利用可再生能源至关重要.
- 现有的WPF数据集的范围有限,缺乏针对单个轮机的详细空间和动态上下文信息.
研究的目的:
- 引入空间动态风力发电预测 (SDWPF) 数据集,这是推动WPF研究的全面资源.
- 为每个风力轮机提供详细的空间分布和动态上下文因素,解决先前数据集的局限性.
- 促进风能预测分析和电网整合战略的改进.
主要方法:
- 开发SDWPF数据集,包括发电,风速,轮机空间分布和动态上下文因素.
- 在数据集中包括轮机特定的天气信息和内部运行状态.
- 利用SDWPF数据集来主办ACM KDD Cup 2022数据挖掘比赛.
主要成果:
- SDWPF数据集丰富了WPF研究的细粒度,多方面的数据.
- 2022年ACM KDD杯,利用SDWPF,吸引了超过2400个全球团队,表明对新型预测解决方案的重大兴趣和潜力.
- 数据集的全面性质支持更准确和更强大的风力发电预测模型.
结论:
- SDWPF数据集代表了风力发电预测研究可用的资源的重大进步.
- 成功的ACM KDD杯2022展示了数据集的价值和潜力,推动数据挖掘和可再生能源方面的创新.
- 通过像SDWPF这样的数据集来增强WPF的能力,对于可靠地将风能集成到电网中至关重要.
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