Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting
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Abstract (arXiv)
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×: 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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 34 references that are in the map. First posted on arXiv 5 Jun 2026.
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11 of 34 references.
References · 11
- 2023A decoder-only foundation model for time-series forecastingcited by 692
- 2025Chronos-2: From Univariate to Universal Forecastingcited by 194
- 2025TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learningcited by 120
- 2025Moirai 2.0: When Less Is More for Time Series Forecastingcited by 69
- 2025From Tables to Time: Extending TabPFN-v2 to Time Series Forecastingcited by 60
- 2024TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecastingcited by 58
- 2017Model-Free Renewable Scenario Generation Using Generative Adversarial Networkscited by 51
- 2024Benchmarking Time Series Foundation Models for Short-Term Household Electricity Load Forecastingcited by 12
- 2024SolNet: Open-source deep learning models for photovoltaic power forecasting across the globecited by 2
- 2026Time Series Foundation Models for Energy Load Forecasting on Consumer Hardware: A Multi-Dimensional Zero-Shot Benchmarkcited by 2
- 2025SPIRIT: Short-term Prediction of solar IRradIance for zero-shot Transfer learning using Foundation Modelscited by 1
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Citing papers per year: 2026: 1.
1 paper.