短期风力发电预测的可移植性:使用爱尔兰和英国的风力发电数据调查模型校准
Cian Deignan1, Juan Manuel González Sopeña2,3, Bidisha Ghosh2
1UCD Centre for Mechanics, Dynamical Systems and Risk Laboratory, School of Mechanical and Material Engineering, University College Dublin, Dublin, D04 V1W8, Ireland.
Scientific reports
|September 30, 2025
概括
这项研究表明,风力发电预测模型可以在风力发电场之间移植. 模型超参数调整,特别是分解模式的数量,影响可移植性和准确性.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在能源中的作用
- 电力系统的信号处理.
背景情况:
- 风能预测 (WPF) 对于将风能整合到电网中至关重要.
- 模型的便携性允许在风电场之间转移校准特征,提高效率.
- 调查超参数的影响是理解和改善WPF模型可移植性的关键.
研究的目的:
- 评估两种混合WPF方法的可移植性:变化模式分解和输送神经网络 (VMD-FFNN) 和集体实证模式分解和输送神经网络 (EEMD-FFNN).
- 检查模型超参数配置对这些方法的预测性能和稳定性的影响.
- 评估WPF模型在爱尔兰和英国的不同风电场数据集中的可转移性.
主要方法:
- 利用爱尔兰和英国风电场的监督控制和数据采集 (SCADA) 数据.
- 实施并比较了两个混合WPF模型:VMD-FFNN和EEMD-FFNN.
- 分析了预测性能对关键模型超参数的敏感性,包括分解模式的数量.
主要成果:
- 无论是VMD-FFNN还是EEMD-FFNN都表现出强大的预测准确度,VMD-FFNN在爱尔兰网站上实现了3.42%的NMAE误差.
- 预测性能对四个检查过的超参数中大约有两个是敏感的.
- 使用有限数量的分解模式 (大约4) 证明足以进行准确的预测,尽管仍然需要特定地点的校准.
结论:
- VMD-FFNN和EEMD-FFNN模型具有良好的准确性和不同程度的可移植性.
- 超参数调整,特别是信号分解模式的数量,对于优化WPF模型可移植性至关重要.
- 增加数据集多样性提高了WPF模型的稳定性,使它们在不同风电场条件下更可靠.
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