一种用于超短时间风速预测的新型组合模型
Shunqing Xu1, Jincai Niu2,3
1School of Electrical Engineering, Yancheng Institute of Technology, Yancheng, 224051, Jiangsu, China.
Scientific reports
|October 21, 2025
概括
这项研究引入了用于风力发电场的先进风速预测模型. 这种新的方法在超短时间的风速预测中达到约97%的准确性.
科学领域:
- 可再生能源系统可再生能源系统
- 计算智能是一种计算智能.
- 数据科学数据科学数据科学
背景情况:
- 精确的超短期风速预测对于高效的风电场运行至关重要.
- 由于数据的复杂性,现有的预测模型往往缺乏足够的精度.
- 分解技术的参数选择可能具有挑战性.
研究的目的:
- 开发一个非常准确的超短时间风速预测模型.
- 为了提高风力发电场控制的风速预测的精度.
- 为了解决当前预测方法的局限性.
主要方法:
- 综合变量模式分解 (VMD),搜索算法 (SSA) 和长短期记忆 (LSTM).
- 使用阿基米德优化算法 (AOA) 优化VMD参数 (模式组件,惩罚因子).
- 优化LSTM参数 (隐藏单位,代,学习率) 使用SSA.
主要成果:
- 拟议的VMD-SSA-LSTM模型在超短时间风速预测方面表现出卓越的性能.
- 使用国家数据浮标中心 (NDBC) 数据的验证证实了该模型的有效性.
- 实现了大约97%的预测准确度.
结论:
- 开发的模型为超短期风速预测提供了一种新且准确的方法.
- 适应性参数优化显著提高了预测模型的性能.
- 这种方法为优化风电场运营提供了有价值的工具.
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