使用贝叶斯优化的机器学习模型预测风速和功率,在埃及的Gabal Al-Zayt
Nehal Elshaboury1, Haytham Elmousalami2,3
1Construction and Project Management Research Institute, Housing and Building National Research Center, Giza, Egypt. nehal.elshabory@hbrc.edu.eg.
Scientific reports
|August 6, 2025
概括
准确的风速和功率预测对于可再生能源至关重要. 机器学习模型,特别是光梯度增强机和袋式决策树,在各种时间尺度上显示出强大的预测性能.
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.
- 气象预报 气象预报
背景情况:
- 准确的风速和功率预测对于高效的可再生能源整合至关重要.
- 现有的预测方法在不同时间尺度的准确性方面面临挑战.
研究的目的:
- 将10种用于预测风速和功率的机器学习技术进行比较和评估.
- 确定最有效的风速预测 (WSP) 和风力预测 (WPP) 模型,跨越各种时间尺度.
主要方法:
- 使用风速和电力集成预测系统.
- 比较单个和整体机器学习模型,包括轻度梯度增强机 (LGBM),极度梯度增强和袋式决策树 (BDT).
- 使用指标评估模型准确性:皮尔森相关系数 (R),解释差异 (EV),平均绝对百分比误差 (MAPE),平均平方误差 (MSE) 和一致性相关系数 (CCC).
主要成果:
- 对于WSP,LGBM,极端梯度提升和BDT,它们的准确性很高 (MAPE:2.64112.274%,R:0.9430.997).
- 对于WPP,LGBM和BDT的预测表现强 (MAPE:0.277186.710%,R:0.9851.000).
- 模型性能在不同的时间尺度上是一致的.
结论:
- 轻度梯度提升机和袋式决策树对于风速和功率预测都非常有效.
- 这些机器学习模型为增强可再生风能应用提供了可靠的解决方案.
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