直接和间接的短期聚合轮机和农场级风力发电预测,整合多个NWP来源
Ghali Yakoub1, Sathyajith Mathew1, Joao Leal1
1University of Agder, Jon Lilletunsvei 9, 4879, Grimstad, Norway.
Heliyon
|November 13, 2023
概括
精确的风力发电预测 (WPF) 对于电网整合至关重要. 汇总轮级预测比农场级预测更能提高准确性,将错误率降低高达15%.
科学领域:
- 可再生能源系统可再生能源系统
- 电气工程 电气工程
- 气象学 天气学
背景情况:
- 风力发电行业的快速扩张给电网整合带来了重大挑战.
- 精确的风力发电预测 (WPF) 对于高效的能源交易,电网平衡和调度操作至关重要.
研究的目的:
- 评估北欧能源市场中轮机级和农场级风力发电预测的有效性.
- 研究风向在间接预测模型中的影响,并比较直接和间接方法.
主要方法:
- 使用三种数值天气预报 (NWP) 和直接和间接预报方法开发了农场级的WPF.
- 实施了一条自动机器学习管道,使用挪威风电场数据集.
- 结合多个WPF,使用基于实时预测错误的权重.
主要成果:
- 间接预测方法,利用风力缩小模型,与原始NWP输出相比,提高了风速预测的准确性.
- 农场级下调模型显示的错误比轮机级模型更小.
- 结合多个NWP来源,直接和间接WPF的预测误差分别减少了8%-30%.
- 聚合的轮级WPF比农场级WPF提高了精度10% (RMSE) 和15% (MAE).
结论:
- 汇总的轮机级风力发电预测比农场级预测更准确.
- 使用多个NWP来源和缩小规模的模型大大减少了预测错误.
- 直接和间接的预测方法都显示了可比的性能,间接方法从风向数据中受益.
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