用合成数据解决光伏冷启动预测难题，让大模型提前上岗
Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting
- 用设备信息和气象数据生成虚拟发电历史，供大模型学习
- 在440个站点测试中，误差比传统方法低1.7到2倍，最佳结果MAE 0.514
- 合成数据来源不影响效果，关键在于时间上下文是否合理
光伏电站投运初期缺乏实际发电数据，难以直接使用常规监督式预测模型。本文提出零样本流程：基于电站元数据和气象协变量生成合成发电历史，使时间序列基础模型（TSFMs）通过推理时条件化进行预测。在严格冷启动基准、真实反馈和自预测反馈三种策略下，对比五种TSFMs与经典基线模型。评估覆盖440个光伏站点，涵盖四个数据集及多样气候区。考虑协变量的基础模型表现优于基线约1.7至2倍：TabPFN-TS在真实反馈下误差最低（MAE 0.514, RMSE 0.721 kWh kWp⁻¹ d⁻¹），Chronos-2在自预测反馈下最稳健。性能对合成历史来源不敏感，表明准确率更多取决于是否存在合理的时间上下文，而非生成器具体细节。
原文摘要 · Abstract (English)
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.
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