用物理约束提升太阳能预测精度，适合边缘设备部署。
Physics-Informed State Space Models for Reliable Solar Irradiance Forecasting in Off-Grid Systems
- 引入物理门控机制，结合太阳天顶角与清晰度指数约束输出。
- 参数少于4万，实现高精度且低延迟的实时预测。
- 专为离网系统设计，避免夜间误报等物理不合理的结果。
离网光伏系统的稳定运行依赖于精确、高效的太阳能预测。现有深度学习模型常因计算开销大且缺乏物理约束，产生不合理预测。本文提出物理感知状态空间模型（PISSM），兼顾效率与物理准确性，适用于边缘微控制器部署。PISSM通过动态汉克尔矩阵嵌入，将原始气象序列转换为鲁棒状态空间，有效滤除传感器噪声；采用线性状态空间模型替代复杂注意力机制，高效建模时间依赖关系并支持并行处理；关键创新在于物理感知门控机制，利用太阳天顶角和清晰度指数结构化约束输出，确保预测严格遵循昼夜周期，杜绝夜间错误。在苏丹恩杜尔曼多年数据集上验证，PISSM以不足4万参数实现优异精度，建立可实时部署的超轻量基准。
原文摘要 · Abstract (English)
The stable operation of off-grid photovoltaic systems requires accurate, computationally efficient solar forecasting. Contemporary deep learning models often suffer from massive computational overhead and physical blindness, generating impossible predictions. This paper introduces the Physics-Informed State Space Model (PISSM) to bridge the gap between efficiency and physical accuracy for edge-deployed microcontrollers. PISSM utilizes a dynamic Hankel matrix embedding to filter stochastic sensor noise by transforming raw meteorological sequences into a robust state space. A Linear State Space Model replaces heavy attention mechanisms, efficiently modeling temporal dependencies for parallel processing. Crucially, a novel Physics-Informed Gating mechanism leverages the Solar Zenith Angle and Clearness Index to structurally bound outputs, ensuring predictions strictly obey diurnal cycles and preventing nocturnal errors. Validated on a multi-year dataset for Omdurman, Sudan, PISSM achieves superior accuracy with fewer than 40,000 parameters, establishing an ultra-lightweight benchmark for real-time off-grid control.
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