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<p>Days-ahead forecasting of photovoltaic (PV) power generation is crucial for pricing and balancing the renewable power grids. The traditional physics-based models offer trade-off interpretability with limited accuracy, whereas the attention-based data-driven models have high predictive accuracy, but limited interpretability. The accurate predictions of PV power are challenging due to stochastic, weather-dependent nature of solar radiation, which induces distribution shifts and non-stationary patterns in the time series data. This paper addresses some of these limitations by proposing a novel physics-guided architecture termed PhysEmbedFormer for forecasting the PV power data in the context of meteorological data. It offers improved interpretability, and forecasting robustness by accounting for the external weather-dependent factors. In particular, the input PV time series are first decomposed into the physics-estimated component and the residual component. The components are jointly embedded into a high-dimensional vector-space using the cross-modality module. The subsequent dual-stage Kolmogorov-Arnold Network (KAN) refinement module represent a learnable non-linear transformation to better match the sample distributions to simpler downstream forecasting models such as the previously proposed iTransformer. Extensive experiments on multiple PV datasets show that PhysEmbedFormer achieves consistently lower MAE, RMSE, and higher <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation> than other competing architectures across different prediction horizons up to 72 hours. At the same time, PhysEmbedFormer experiences the second narrowest 95% confidence interval in its predictions, so it is also robust to sample distribution shifts due to changes in the present weather conditions.</p>

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PhysEmbedFormer: a physics-guided interpretable architecture for days-ahead forecasting of PV power

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月29日

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  • Yue Yu,
  • Pavel Loskot,
  • Yu Gao

摘要

Days-ahead forecasting of photovoltaic (PV) power generation is crucial for pricing and balancing the renewable power grids. The traditional physics-based models offer trade-off interpretability with limited accuracy, whereas the attention-based data-driven models have high predictive accuracy, but limited interpretability. The accurate predictions of PV power are challenging due to stochastic, weather-dependent nature of solar radiation, which induces distribution shifts and non-stationary patterns in the time series data. This paper addresses some of these limitations by proposing a novel physics-guided architecture termed PhysEmbedFormer for forecasting the PV power data in the context of meteorological data. It offers improved interpretability, and forecasting robustness by accounting for the external weather-dependent factors. In particular, the input PV time series are first decomposed into the physics-estimated component and the residual component. The components are jointly embedded into a high-dimensional vector-space using the cross-modality module. The subsequent dual-stage Kolmogorov-Arnold Network (KAN) refinement module represent a learnable non-linear transformation to better match the sample distributions to simpler downstream forecasting models such as the previously proposed iTransformer. Extensive experiments on multiple PV datasets show that PhysEmbedFormer achieves consistently lower MAE, RMSE, and higher \(R^2\) than other competing architectures across different prediction horizons up to 72 hours. At the same time, PhysEmbedFormer experiences the second narrowest 95% confidence interval in its predictions, so it is also robust to sample distribution shifts due to changes in the present weather conditions.