光伏运维团队终于有了冷启动预测的实用方案——无需历史数据即可用基础模型实现高精度预测，做新能源发电预测的开发者可以直接参考TabPFN-TS和Chronos-2的表现。
光伏电站在投运初期缺乏历史观测数据，导致标准监督预测方法无法直接使用。研究者提出一种零样本流程，利用电站元数据和气象协变量生成合成生产历史，使时间序列基础模型（TSFMs）能够通过推理时条件进行预测。在440个光伏站点、四个数据集和多种气候条件下，五种TSFMs与经典基线对比，协变量感知模型性能提升约1.7-2倍，其中TabPFN-TS在真实反馈策略下误差最低（MAE 0.514），Chronos-2在自预测反馈策略下最鲁棒。合成历史来源对性能影响不大，表明合理的时序上下文比具体生成器更重要。
Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting
At commissioning time, Photovoltaic (PV) operators must forecast production before target-site observations are available, limiting the direct use of standard supervised forecasters. This cold-start setting is addressed with a zero-shot pipeline that generates a synthetic production history from plant metadata and meteorological covariates, enabling time-series foundation models (TSFMs) to forecast through inference-time conditioning. Five TSFMs are benchmarked against classical baselines under strict Cold-Start Baseline, Real Feedback, and Self-Forecast Feedback strategies. The evaluation spans $440$ PV sites across four datasets and diverse climate regimes. Covariate-aware foundation models outperform baselines by approximately $1.7-2\times$: TabPFN-TS achieves the lowest error under Real Feedback (MAE $0.514$, RMSE $0.721$ $kWh$ ${kWp}^{-1}$ ${d}^{-1}$), while Chronos-2 is most robust under Self-Forecast Feedback. Performance is largely insensitive to the synthetic-history source, indicating that accuracy is driven more by the availability of plausible temporal context than by the specific generator.