PARA-PV: Physics-Aware Retrieval-Augmented PV Prediction Based on Frozen Foundation Model and Distribution Shift Correction
Quick Answer
PARA-PV introduces a Physics-Aware Retrieval-Augmented framework for PV power forecasting, enhancing accuracy by integrating physical knowledge and correcting distribution shifts.
Quick Take
The model utilizes a frozen Chronos time-series foundation and a physics-constrained loss function to adapt predictions across varying weather and operational conditions.
Key Points
- PARA-PV encodes multivariate PV observations into patch-level representations.
- The framework retrieves historical patches consistent with current PV conditions.
- A distribution shift correction module adjusts forecasts based on weather and time.
- The model employs a physics-constrained loss function to prioritize critical operational states.
- Code for PARA-PV is publicly available for further research.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Accurate photovoltaic (PV) power forecasting is essential for reliable grid dispatch and renewable energy integration, yet it remains challenging because PV generation is jointly shaped by weather variability, day-night transitions, regime-dependent dynamics, and strict physical constraints. We propose PARA-PV, a Physics-Aware Retrieval-Augmented framework that embeds physical knowledge throughout the forecasting process. The framework first encodes multivariate PV observations into patch-level representations and, through a physics-aware retrieval-augmented learner, retrieves historical patches and analog trajectories that are consistent with the current window in temporal shape, power level, PV operating state, and intra-day period; this yields a physically grounded base forecast. To supplement local memory with broader temporal knowledge, the base forecast is then calibrated against a frozen Chronos time-series foundation-model prior through a lightweight residual adapter, so that general temporal regularities are adapted to PV-specific dynamics without overriding the physically grounded prediction. Because residual conditional distribution shifts persist when weather and diurnal regimes change, a physics-aware distribution shift correction module subsequently adjusts the preliminary forecast using power, weather, timestamp, and day/night conditions, applying gated mean-shift and scale corrections selectively. Finally, a physics-constrained loss function partitions the samples into peak, ramping, night-time, and regular regimes and adaptively reweights their error contributions, preventing the dominant regular regime from suppressing learning of operationally critical states. Our code is available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.08079 [cs.AI] |
| (or arXiv:2607.08079v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.08079 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Weican Liu [view email]
[v1]
Thu, 9 Jul 2026 03:15:42 UTC (7,674 KB)
— Originally published at arxiv.org
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