使用堆叠元模型和缩小维度的方法,优化极端高度的光伏功率预测
Saul Huaquipaco1, Wilson Mamani2, Norman Beltran3
1School of Ingeniería de Sistemas e Informática Faculty of Engineering, Universidad Nacional de Moquegua, Moquegua, Peru. shuaquipacoe@unam.edu.pe.
Scientific reports
|November 3, 2025
概括
现在可以准确地预测高海拔光伏 (PV) 系统的活性功率. 一个新的混合模型克服了数据挑战,实现了超过99.9%的准确性,可靠地预测太阳能.
科学领域:
- 可再生能源系统可再生能源系统
- 机器学习应用 机器学习应用
- 环境监测 环境监测
背景情况:
- 在极端高度 (>3800米) 的光伏 (PV) 系统. 由于气候变化和设备问题,它们面临着预测挑战.
- 传统的预测模型在高山环境中常见的非静止数据中扎.
- 数据碎片化和环境复杂性阻碍了在山区准确预测功率输出.
研究的目的:
- 开发一个强大的混合堆叠元模型,用于高海拔光伏系统中准确的活性功率预测.
- 在处理非静态气候数据和数据碎片化时,解决现有模型的局限性.
- 提高在极端地理环境下对光伏装置发电预测的可靠性.
主要方法:
- 开发了一个四阶段混合堆叠元模型,结合了适应性预处理来进行时间序列重建.
- 使用顺序特征选择 (SFS) 和主要组件分析 (PCA) 来减少维度.
- 一个整体方法集成线性规范化 (Lasso/Ridge) 与梯度增强模型 (LightGBM/CatBoost).
主要成果:
- 基于LightGBM的元模型取得了非常高的准确性,R2=99.9858%,MAE=6.76,MSE=13.66.
- 拟议的模型在50次代内表现出稳定的趋同,培训-验证差异最小.
- 在预测准确度方面,LightGBM元模型显著超过了CatBoost和普通最小平方 (OLS) 模型.
结论:
- 开发的混合堆叠元模型,特别是LightGBM变体,有效地解决了数据碎片化和环境复杂性,用于高海拔光伏电力预测.
- 缩小尺寸技术 (PCA/SFS) 和双叠架构 (线性+增强) 之间的协同作用对于该模型的成功至关重要.
- 在具有挑战性的山区环境中,LightGBM模型显示出可靠的能源预测解决方案的希望,因此需要进一步调查其通用性.
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