PARA-PV: Physics-Aware Retrieval-Augmented PV Prediction Based on Frozen Foundation Model and Distribution Shift Correction

2026-07-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Energy Systems Forecasting · Depth: Expert, extended

Summary

PARA-PV is a novel Physics-Aware Retrieval-Augmented framework designed for accurate multi-step photovoltaic (PV) power forecasting. It addresses challenges from weather variability and physical constraints by integrating four core components. First, a Physics-Aware Retrieval-Augmented Learner (PA-RAL) retrieves physically consistent historical patterns from a memory bank. Second, a lightweight residual adapter calibrates these predictions using a frozen Chronos-2 time-series foundation model (120M parameters) as a general temporal prior. Third, a Physics-Aware Distribution Shift Correction (PA-DSC) module adjusts forecasts for regime-dependent biases using weather scores and day/night indicators. Finally, a physics-constrained loss function adaptively reweights errors across peak, ramping, night-time, and regular regimes. Experiments on two PV farms (50 MW and 35 MW, 2019-2020 data) demonstrate PARA-PV consistently outperforms seven baselines, achieving lower point-forecast errors and sharper prediction intervals with under 1M trainable parameters (993.22K).

Key takeaway

For AI Scientists and Machine Learning Engineers developing renewable energy forecasting solutions, PARA-PV offers a robust approach to improve PV power predictions. You should consider integrating physics-aware retrieval, foundation model priors, and distribution shift correction to enhance accuracy and reliability, especially for critical operational states. This framework demonstrates that balancing general temporal knowledge with domain-specific physical constraints can yield superior performance with efficient parameter usage, crucial for grid dispatch and energy management.

Key insights

PV forecasting benefits from integrating physical constraints and foundation model priors with retrieval-augmented learning.

Principles

• Physical constraints improve PV forecast consistency.
• Foundation models offer broad temporal priors.
• Adaptive loss functions balance regime-specific errors.

Method

PARA-PV encodes PV observations into patches, retrieves physically consistent historical patterns, calibrates with a frozen Chronos prior via a residual adapter, and applies gated distribution shift corrections using a dilated temporal CNN.

In practice

• Use patch-level representations for retrieval.
• Clip foundation model priors to physical bounds.
• Segment loss by PV operating regimes.

Topics

• PV Power Forecasting
• Retrieval-Augmented Learning
• Time-Series Foundation Models
• Physics-Aware AI
• Distribution Shift Correction
• Renewable Energy Integration

Code references

• weican1103/PARA-PV

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.