这篇论文用具体模型和实验告诉你，哪些深度模型在光伏功率预测里更扛NWP天气误差，不是空谈。
该论文提出一个基于仿真的物理约束鲁棒性评估框架，使用虚拟光伏功率作为受控响应变量，研究NWP预测误差对模型的影响。评估了PatchTST、GRU、N-HITS、LightGBM等六种模型在动态NWP扰动下的表现。结果表明，在中等至高等干扰下，序列模型（如PatchTST、GRU）比LightGBM基线具有更强的噪声滤波和时间鲁棒性。SHAP和IG分析显示，模型在输入扰动时会将预测依据从未来预报转向历史观测和物理先验。Pareto分析综合了纯净精度、鲁棒性和计算延迟，为工程选型提供了参考。
Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically Interpretable Analysis
Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs. In photovoltaic (PV) forecasting, this requirement is especially challenging because numerical weather prediction (NWP) errors are temporally correlated, state dependent, and physically coupled across variables. Existing evaluations, however, often rely on perfect forecast assumptions or simplistic perturbations that do not reflect these characteristics. This study presents a physically constrained robustness evaluation framework based on simulation, using virtual PV power as a controlled response variable to isolate the propagation of input uncertainty from confounders at the plant level. Six representative machine learning and deep sequence models, including PatchTST, GRU, N-HITS, and LightGBM, are evaluated under dynamic NWP perturbations with heteroscedasticity modulated by clear-sky conditions and Erbs reconstruction that preserves radiation consistency. The results show that sequence models provide stronger noise filtering and temporal resilience than a strong tabular baseline under medium to high disturbance regimes. SHapley Additive exPlanations (SHAP) and Integrated Gradients (IG) further support a feature reallocation tendency at the case level, in which predictive reliance shifts from corrupted future forecasts toward more stable historical observations and deterministic physical priors. A Pareto analysis of accuracy under clean conditions, robustness, and computational latency then translates these findings into engineering implications for robustness assessment and model selection under forecast uncertainty.