PARA-PV: Physics-Aware Retrieval-Augmented PV Prediction Based on Frozen Foundation Model and Distribution Shift Correction
arXiv:2607.08079v1 Announce Type: new Abstract: 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 retriev
The increasing integration of renewable energy sources like PV into national grids necessitates more accurate forecasting methods to ensure stability, especially with advancements in AI foundation models making such solutions viable.
Accurate PV power forecasting is critical for grid stability, efficient energy trading, and furthering renewable energy adoption, directly impacting the reliability and cost-effectiveness of sustainable power systems.
The application of physics-aware retrieval-augmented AI to PV forecasting offers a more robust and reliable method, potentially reducing energy wastage and improving grid management compared to previous models.
- · Renewable energy operators
- · Grid management companies
- · AI model developers
- · Energy consumers
- · Inefficient energy forecasting companies
- · Fossil fuel proponents (marginal)
Improved PV forecasting leads to better grid balancing and reduced reliance on dispatchable power sources.
Enhanced grid stability and lower operational costs accelerate the transition to a higher penetration of renewable energy.
Increased renewable energy integration reduces carbon emissions and contributes to energy independence goals for nations.
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Read at arXiv cs.AI