基于变化模式分解和先进的机器学习模型的风速预测在埃及扎阿法拉纳
Ali Taha1, Nathalie Nazih2, Peter Makeen3
1Electrical Engineering Department, Faculty of Engineering, The British University in Egypt (BUE), Al Shorouk City, Egypt. Ali.Taha@bue.edu.eg.
准确的长期风速预测对于可再生能源至关重要. 这项研究将变化模式分解 (VMD) 与机器学习模型相结合,显示LightGBM实现了98%的精度,以更好地管理风能.
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
- 机器学习 机器学习
背景情况:
- 风能是重要的清洁能源来源,但其整合受到不准确的长期风速预测的阻碍.
- 现有的预测模型与风速数据的非线性复杂性作斗争,导致能源分配挑战.
研究的目的:
- 通过将变量模式分解 (VMD) 与先进的机器学习算法集成,提高长期风速预测的准确性.
- 为了评估VMD与极端梯度增强 (XGBoost),自适应增强 (AdaBoost),光梯度增强机器 (LightGBM),K-近邻 (KNN) 和Informer模型相结合的性能.
主要方法:
- 一种多步骤的方法,将VMD用于数据分解与集体和基于变压器的机器学习模型相结合.
- 利用来自NASA Power项目35,000个风速数据样本进行培训和验证.
- 使用R平方 (R2) 评分和各种错误指标来评估性能.
主要成果:
- 与VMD相结合的LightGBM实现了最高的R2分数98%和最低的错误指标,证明了优异的预测性能.
- XGBoost和KNN模型也表现出强的结果,R2得分为97%.
- 虽然Informer模型先进,但其性能最低,R2得分为78%.
结论:
- 变化模式分解有效地解决了风速数据固有的复杂性.
- 将VMD等分解技术与先进的机器学习模型相结合,可以显著提高长期风速预测的准确性.
- 这种方法为优化风能管理和整合提供了一个有希望的解决方案.
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