AI 中文总结
研究数值天气预报误差下光伏功率预测深度学习模型的鲁棒性，提出基于模拟的评估框架，用虚拟光伏功率隔离不确定性传播。评估六个模型，发现序列模型在中高干扰下表现更好，经分析得出对鲁棒性评估和模型选择的工程意义。
AI 中文摘要
人工智能预测模型的工程应用不仅需要高标称精度，还需要在不确定输入下具有可预测的行为。在光伏预测中，由于数值天气预报（NWP）误差在时间上相关、状态依赖且跨变量物理耦合，这一要求尤其具有挑战性。现有评估往往依赖完美预测假设或简单扰动，无法反映这些特征。本研究提出了一个基于模拟的物理约束鲁棒性评估框架，使用虚拟光伏发电功率作为受控响应变量，以在电站层面将输入不确定性的传播与混杂因素隔离开来。在由晴空条件调制异方差性并保留辐射一致性的Erbs重建的动态NWP扰动下，对包括PatchTST、GRU、N-HITS和LightGBM在内的六个代表性机器学习和深度序列模型进行了评估。结果表明，在中到高干扰情况下，序列模型比强大的表格基线提供更强的噪声过滤和时间弹性。SHapley加法解释（SHAP）和集成梯度（IG）进一步支持了案例层面的特征重新分配趋势，即预测依赖从错误的未来预测转向更稳定的历史观测和确定性物理先验。然后，对清洁条件下的精度、鲁棒性和计算延迟进行帕累托分析，将这些发现转化为预测不确定性下鲁棒性评估和模型选择的工程意义。
英文摘要
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.
