C. Zhang, X. Fu, D. Qiu, H. Badihi, and H. Gu, “Robust imputation of missing photovoltaic power data using a weather- and context-aware hybrid transformer framework,” Renewable Energy, vol. 256, 2026.
New hybrid transformer framework for photovoltaic power data imputation published in Renewable Energy
Accurate and robust handling of missing photovoltaic (PV) power data is essential for reliable energy forecasting, monitoring, and control. A new journal article, now published in Renewable Energy, presents a novel hybrid transformer-based framework that significantly enhances PV power data imputation, especially under extreme missing data conditions.
The model combines a weather-prompted and context-aware mechanism with a coarse-to-fine imputation pipeline. By integrating external meteorological features and leveraging diagonal masked self-attention, the framework can infer missing data with high robustness.