一种基于多交互优化信息模型的风能预测方法的新设计
Wenjuan Zhou1,2, Feng Huang1,2, Bing Wei1,2
1Hunan Institute of Engineering, School of Electrical and Information Engineering, 88 Fuxing East Road, Yuetang, Xiangtan, Hunan, China.
PloS one
|August 28, 2025
概括
MFIO-Informer模型通过优化多源功能交互和数据健康,提高准确性和速度来提高风能预测.
科学领域:
- 可再生能源系统
- 在能源领域的人工智能
- 对于电网的机器学习
背景情况:
- 精确的风能预测对于电网的稳定性至关重要.
- 由于特征合和数据健康问题,传统的神经网络和Informer模型在复杂的条件下面临限制.
- 现有的方法难以预测准确性和计算效率.
研究的目的:
- 提出一个新的预测框架,MFIO-Informer,用于增强风能预测.
- 通过优化多源功能交互和数据健康感知来提高预测准确性和计算效率.
- 在复杂的操作环境中解决Informer模型的局限性.
主要方法:
- 使用拉索和皮尔森相关性进行特征选,以确定关键的多源特征.
- 一个完全连接的神经网络 (FNN) 来提取反映设备性能的动态协同系数 (DSC).
- 使用历史功率数据和DSC生成Informer模型的优化矩阵进行数据健康评估.
- 实施MFIO-Informer框架,整合功能优化和数据健康感知.
主要成果:
- 在两个公共风能数据集上,MFIO-Informer模型表现出卓越的性能.
- 与传统的Informer模型相比,实现了大约20%的预测精度.
- 实现了54.85%更快的预测速度,表明了显著的计算效率增长.
结论:
- 拟议的MFIO-Informer框架有效地解决了现有的风能预测模型的局限性.
- 整合物理特征协作分析和数据健康状况感知可显著提高预测准确性和速度.
- MFIO-Informer为稳定高效的风电网集成提供了一个有前途的解决方案.
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