Improving Photovoltaic Energy Systems using Physics-Guided Hybrid Forecasting Model
In control engineering, we search for stability and accuracy. But we chose to confront the most complex non-linear system: the Earth's atmosphere.
The atmosphere is a chaotic dynamical system — a suspended speck of dust in Sudan's skies can overturn the energy balance in seconds.
We prove that merging atmospheric physics with deep AI can decode this chaos and transform reactive systems into proactive ones.
Design a system that anticipates atmospheric volatility instead of merely reacting to it — using physics-guided AI and hierarchical control.
Toggle between the old reactive approach and our proactive solution.
Dust storms (Haboob) cause GHI drops exceeding 60% in minutes, destroying grid stability.
Standard AI predicts yesterday's weather instead of tomorrow's — useless for control.
Systems respond after changes occur. Always one step behind — energy waste and degradation.
Transformer models (>2M params) are too heavy for real-time edge deployment.
CNN-BiLSTM with 15 physics features. 7 are zero-cost from solar geometry.
Level 1: Reference model. Level 2: Lightweight surrogate.
MPC uses future predictions to act before weather events impact the system.
Fractional-Order PID with (λ, μ) provides superior flexibility over integer PID.
Left: Chaotic atmospheric data — unpredictable motion.
Right: Physics-guided flow — our hybrid model imposing constraints.
CNN: Extracts spatial patterns from atmospheric data with high precision.
BiLSTM: Comprehends temporal sequencing over time and uncovers complex dependencies.
The introduction is complete. Explore the control system or dive into the chapters.