A hybrid XGBoost-LSTM model with physics-informed features and uncertainty quantification for solar power forecasting

Accurate short-term photovoltaic (PV) power forecasting is essential for grid stability and efficient PV-grid coordination. However, many conventional learning pipelines remain vulnerable to pervasive missing data, irregular sampling, and the absence of calibrated uncertainty estimates. This paper p...

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Автори:	Kayes Bin Yousuf, Ashrafi Akter, Hadid Ahmed Noor, Ashraful Hoque, Ashik Ahmed
Формат:	Artigo
Мова:	Inglês
Опубліковано:	Elsevier 2026-06-01
Серія:	Energy Reports
Предмети:	Solar forecasting Hybrid deep learning XGBoost LSTM Uncertainty quantification Renewable energy
Онлайн доступ:	http://www.sciencedirect.com/science/article/pii/S2352484726000375
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A hybrid XGBoost-LSTM model with physics-informed features and uncertainty quantification for solar power forecasting

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