增强的多地平线光伏电力预测:一种新的方法,将ICEEMDAN分解与层次频率神经网络集成在一起
Yaopeng Han1,2,3,4, Chenxi Li1, Siqi Chen1
1College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, Liaoning, China.
PloS one
|November 14, 2025
概括
本研究介绍了一种新的混合太阳能预测模型,使用数据分解和频率分层预测. 这种先进的模型显著提高了可再生能源发电厂运行的准确性和稳定性.
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
- 时间序列预测时间序列预测
背景情况:
- 太阳能光伏 (PV) 电力对于可再生能源至关重要,但预测其复杂的动态是具有挑战性的.
- 现有的模型经常使用单一的范式,缺乏足够的时间信息表示,限制了准确性.
- 准确的太阳能光伏发电预测对于电网稳定性和高效的能源管理至关重要.
研究的目的:
- 为增强太阳能光伏发电预测提出一种新的混合模型.
- 解决现有模型关于非静止性和时间依赖性的局限性.
- 为稳健的发电厂运行提供准确的概率预测间隔.
主要方法:
- 为了非静止性,采用了改进的完整合奏实证模式分解与适应噪声 (ICEEMDAN).
- 引入了一个频率分层的异质网络,用于组件智能建模.
- 综合改进的相对定位编码 (IRPE) 用于时间依赖性和定量回归用于概率间隔.
主要成果:
- 拟议的模型实现了卓越的性能,在DKASC和太阳能I数据集上表现比最近的基准标准高14.6%和8.1%.
- 在MIMO策略下实现了0.1142 (120分钟) 和0.1490 (2880分钟) 的正常化nMAE值.
- 在各种天气条件下通过威尔科克森签名级别测试和不确定性分析证明了统计稳定性和稳健性.
结论:
- 新的混合模型显著提高了太阳能光伏发电预测的准确性和稳定性.
- 该模型能够捕捉复杂的动态并提供可靠的预测,从而支持发电厂的运营和规划.
- 这种方法为预测可再生能源的挑战提供了可靠的解决方案.
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