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基于ICEEMDAN,MFE,LSTM和告知者的混合模型的短期风速预测
Wang Xinxin1, Shen Xiaopan1, Ai Xueyi1
1School of Management, Wuhan University of Science and Technoloy, Wuhan, China.
PloS one
|September 8, 2023
概括
准确的风速预测对于电力系统的稳定性至关重要. 一个结合ICEEMDAN,MFE,LSTM和Informer的新混合模型证明了风能预测的卓越准确性.
科学领域:
- 可再生能源系统可再生能源系统
- 动力系统的稳定性 动力系统的稳定性
- 计算智能是一种计算智能.
背景情况:
- 风能是重要的可再生能源,但由于风速的变化,其整合面临着挑战.
- 风速的随机性和间歇性会影响电力系统的安全性和稳定性.
- 准确的风速预测对于有效的电力系统运行和电网管理至关重要.
研究的目的:
- 为准确预测风速提出一种新的混合模型.
- 提高风力发电预测的可靠性和适用性.
- 为应对电力系统中风速间歇性带来的挑战.
主要方法:
- 利用改进的完整组合实证模式分解与适应噪声 (ICEEMDAN) 来将风速数据分解为内在模式函数 (IMF).
- 应用多尺度模糊 (MFE) 来分析每个IMF的复杂性和具有相似MFE值的聚合模式.
- 采用长短期记忆 (LSTM) 和Informer模型进行后续预测,为每个后续选择优秀的执行者.
- 叠加单个后续预测,生成最终的风速预测.
主要成果:
- 拟议的混合模型与其他七种相关模型相比,显示出更高的准确性.
- 该模型在不同的预测期内显示出显著的有效性和适用性.
- 实验结果证实了ICEEMDAN-MFE-LSTM-Informer混合方法的有效性.
结论:
- 开发的混合动力模型为准确的风速预测提供了强大的解决方案.
- 这种方法有效地减轻了风速间歇性对电力系统的影响.
- 这些发现支持风能更广泛的采用和整合.
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