一种基于深度学习和组合预测的风速间隔预测方法
XueJun Chen1,2, Tao Han3, Peng Cheng1
1Gansu Meteorological Service Center, Lanzhou, 730020, Gansu, China.
Scientific reports
|July 2, 2025
概括
间隔预测 (CMIF) 的新组合方法提高了风速预测的准确性. 这种方法可以改善风力轮机运行和电网管理的实时预测.
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
- 时间序列分析时间序列分析
背景情况:
- 准确的风速预测对于高效的风力轮机运行和电网稳定性至关重要.
- 现有的方法经常与风速数据固有的混乱性质和噪音作斗争.
- 量化风速的不确定性对于可靠的能源管理至关重要.
研究的目的:
- 引入联合间隔预测方法 (CMIF) 进行增强的实时风速不确定性预测.
- 提高风速间隔预测的准确性和可靠性.
- 为了促进更好的风力轮机运行和电网调度.
主要方法:
- 利用时间变化的过来进行实证模式分解和相位空间重建,以解决混乱现象和噪声.
- 考虑并选择高性能统计和机器学习模型.
- 采用多目标优化器来组合选定的模型进行最终预测.
主要成果:
- 与单个模型相比,CMIF在预测风速间隔准确度方面取得了显著的改进,从1.07%到55.37%不等.
- 该方法实现了狭窄的预测间隔,同时保持了高覆盖率.
- 实验验证是使用甘风力发电塔数据进行的.
结论:
- CMIF有效地提高了风速间隔预测的准确性.
- 该方法提供了一种可靠的方式来量化风速的不确定性.
- CMIF支持提高风力轮机和电网的运营效率.
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