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Updated: Jan 31, 2026

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PhysEmbedFormer:一种以物理为导向的可解释架构,用于日前预测光伏发电量
Yue Yu1, Pavel Loskot2, Yu Gao3
1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310058, China.
Scientific reports
|January 29, 2026
概括
PhysEmbedFormer通过结合物理和数据驱动方法来增强光伏 (PV) 功率预测. 这种以物理为导向的架构提高了预测准确性和对天气变化的稳定性.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能的人工智能
- 时间序列分析时间序列分析
背景情况:
- 准确的光伏 (PV) 电力预测对于电网稳定性和能源定价至关重要.
- 传统模型缺乏准确性或可解读性;数据驱动模型与天气诱导的数据转移作斗争.
研究的目的:
- 介绍PhysEmbedFormer,这是一个新的物理引导架构,用于强大的光伏电力预测.
- 通过整合气象数据和基于物理的组件来提高解释性和准确性.
主要方法:
- 光伏时间序列的分解为物理估计和残余组件.
- 组件的联合嵌入使用跨模式模块.
- 通过双阶段的科尔摩戈罗夫-阿诺德网络 (KAN) 进行精炼,以配送匹配.
主要成果:
- 与现有架构相比,PhysEmbedFormer实现了较低的MAE和RMSE,以及更高的R2.
- 对分布变化和狭窄的预测间隔 (95% CI) 证明了稳定性.
- 在多个光伏数据集中,高达72小时的有效预测.
结论:
- PhysEmbedFormer为光伏电力预测提供了一个强大的和可解释的方法.
- 这种以物理为导向的方法有效地处理了依赖于天气的太阳辐射变化.
- 这种架构改善了电网集成和可再生能源管理.
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