MCrossFormer:用于光伏发电和寿命预测的多级跨度变压器
JiaWen Sun1, WenZhong Yang2, YaBo Yin1
1School of Computer Science and Technology, Xinjiang University, Urumqi, 830046, China.
Scientific reports
|December 16, 2025
概括
这项研究引入了一个新的深度学习框架,MCrossFormer,用于准确的光伏 (PV) 功率预测. 该模型有效地捕捉复杂的降解模式和多尺度的时间数据,提高光伏系统的可靠性.
科学领域:
- 可再生能源工程可再生能源工程
- 人工智能在能源中的作用
- 材料科学用于光伏发电.
背景情况:
- 准确预测光伏 (PV) 模块寿命和输出功率对于系统可靠性和经济可行性至关重要.
- 挑战包括由环境因素影响的复杂降解机制和光伏功率数据的多尺度时间特征.
研究的目的:
- 通过将物理降解模型与深度学习相结合,开发一种完善的光伏功率预测的综合方法.
- 提出一种新的多级跨度变压器 (MCrossFormer) 架构,以提高预测准确性和概括性.
主要方法:
- 对环境因素 (温度,湿度,紫外线辐射,热循环) 对光伏模块降解的系统分析.
- 开发一个神经网络模型,以捕捉光伏功率数据中的多尺度时间模式.
- 介绍 MCrossFormer 架构,并行编码器-解码器结构和长短距离注意力机制.
主要成果:
- 该MCrossFormer架构有效地捕捉了光伏功率数据的趋势,周期和接近性特征.
- 提出的注意力机制可以动态识别光伏功率时间序列中的关键模式.
- 对公共基准的实验表明,与最先进的模型相比,有显著和持续的改进.
结论:
- 综合方法和MCrossFormer架构为光伏电力预测提供了强大的解决方案.
- 该模型的有效性在实际预测场景中得到验证,提高了光伏系统的可靠性和经济可行性.
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