一种基于LSTM-XGBoost-EEDA-SO模型的光伏功率预测方法
Ying Xu1, Xinrong Ji2, Zhengyang Zhu3
1State Grid Wuxi Power Supply Company, Wuxi, 214111, China. tubaoyueyue@163.com.
Scientific reports
|August 18, 2025
概括
精确的光伏 (PV) 功率预测通过一种新的混合模型得到了改进. 该方法使用信号分解和并行预测与优化权重,显著减少错误,以更好地管理电网.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在电力工程中的应用
- 用于时间序列分析的信号处理.
背景情况:
- 光伏发电对气象条件非常敏感,需要准确的预测,以确保稳定的电力系统运行和经济的调度.
- 现有的光伏电力预测方法经常与天气变化影响的复杂,非线性动态作斗争.
- 提高光伏电力预测的精度对于电网整合和可靠的能源供应至关重要.
研究的目的:
- 开发一个混合框架,用于高精度的光伏电力预测.
- 通过整合先进的信号分解,并行预测技术和适应性重量优化来提高光伏功率预测的准确性.
- 为了应对在光伏发电中气象波动带来的挑战.
主要方法:
- 一个混合框架,将Thompson-Tau-Newton插值用于数据预处理和Pearson相关性用于特征选择.
- 集体实证模式分解 (EEMD) 用于将光伏功率序列分解为基于样本的多尺度低频和高频组件.
- 一个并行的XGBoost-LSTM预测结构,其中XGBoost模型低频组件和LSTM模型高频组件.
- 蛇优化 (SO) 算法用于组合权重的动态优化,用于预测结果的自适应融合.
主要成果:
- 拟议的混合模型在PV电力预测准确性方面明显优于独立基准方法.
- 与粒子群优化 (PSO),搜索算法 (SSA) 和等重分配相比,SO算法实现了较低的预测错误.
- 分解和并行预测方法有效地捕获了光伏功率数据中的趋势和时间依赖.
- 适应性重量优化确保了不同组件预测结果的卓越融合.
结论:
- 拟议的混合框架为高精度光伏电力预测提供了一种新且有效的方法.
- 整合多模式特征融合和优化重量分配是提高预测性能的关键.
- 该方法在处理对光伏发电的气象影响方面表现出卓越的能力.
- 这项研究有助于更可靠,更有效地将太阳能集成到电网中.
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