Author

Muhammad Tahir Hassan

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#artificial intelligence Preprint Sep 2026

Physics-Informed State Space Models for Day-Ahead Photovoltaic Power Forecasting

Accurate day-ahead photovoltaic (PV) power forecasting requires both strong temporal modeling and adherence to physical PV system constraints. Purely data-driven models often ignore the underlying PV conversion process and may produce predictions that are outside the operational range of a PV system. In this paper, we...

Phil Aupke, M. Hassan, Khalid Ali et al. · 0 citations

Open access Aug 2026

Physics-Guided Temporal Fusion Transformer for High-Resolution Photovoltaic Power Forecasting Across Heterogeneous Solar Stations

Accurate high-resolution photovoltaic power forecasting remains challenging because rapid weather changes and heterogeneous plant configurations produce nonlinear variations and extreme errors. Existing data-driven methods capture complex meteorological relationships but may generate physically inconsistent forecasts...

M. Nasir, Muhammad Farhan Hanif, Muhammad Tahir Hassan et al. · Clean Energy · 0 citations