Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting

2026-06-05

AI Summary

This study addresses the cold-start forecasting challenge in newly commissioned photovoltaic (PV) power plants, where the absence of historical generation data hinders accurate prediction. To overcome this limitation, the authors propose a zero-shot forecasting approach that integrates physics-informed synthetic history with covariate-aware time series foundation models. Specifically, plant metadata and meteorological covariates are leveraged to generate physically guided synthetic time series, which serve as contextual conditioning during inference. The framework uniquely combines five state-of-the-art time series foundation models—including TabPFN-TS and Chronos-2—with this synthesis strategy and incorporates multiple feedback mechanisms. Evaluated across 440 real-world PV sites, the method substantially outperforms conventional baselines by 1.7–2× in accuracy. Notably, TabPFN-TS achieves a mean absolute error (MAE) of 0.514 kWh·kWp⁻¹·d⁻¹ under real feedback, while Chronos-2 demonstrates the most robust performance under self-feedback.

Abstract

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.

Problem

• cold-start
• photovoltaic forecasting
• time series foundation models
• synthetic history
• zero-shot forecasting

Innovation

• Physics-Informed Synthetic Histories
• Time Series Foundation Models
• Cold-Start Forecasting
• Zero-Shot PV Prediction
• Inference-Time Conditioning