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.

翻译：在光伏电站投运初期，运营商必须在获取目标站点观测数据之前预测发电量，这限制了标准监督预测方法的直接应用。针对这一冷启动场景，本文提出了一种零样本预测流程：通过电站元数据和气象协变量生成合成发电历史数据，使时间序列基础模型（TSFMs）能够通过推理时条件化进行预测。在严格冷启动基线、真实反馈和自预测反馈三种策略下，我们对五种TSFMs与经典基线模型进行了基准测试。评估涵盖440个光伏站点，涉及四个数据集和多种气候区域。协变量感知的基础模型相较于基线模型性能提升约1.7-2倍：TabPFN-TS在真实反馈策略下取得最低误差（MAE 0.514，RMSE 0.721 kWh kWp⁻¹ d⁻¹），而Chronos-2在自预测反馈策略下表现出最强鲁棒性。模型性能对合成历史数据来源不敏感，表明预测精度更多取决于合理时间上下文信息的可用性，而非特定生成器。