AI Concentrated Solar Aim Optimization
Optimizes heliostat aiming and field control with AI to increase receiver efficiency and reduce thermal stress.
The Problem
“Maximize CSP output via real-time heliostat aiming”
Organizations face these key challenges:
Flux hot spots and non-uniformity drive receiver tube damage risk, conservative derates, and accelerated fatigue
Static aiming tables cannot keep up with transient conditions (wind, clouds) and gradual drift (alignment, soiling), causing chronic spillage and lost thermal input
Manual tuning and calibration campaigns are time-consuming, require specialized expertise, and often produce plant-specific settings that degrade between updates
Impact When Solved
The Shift
Real-World Use Cases
AI in Energy Industry: Smart Grid Optimization and Energy Management
This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.
Artificial Intelligence in Renewable Energy Optimization
This is like giving a wind farm or solar plant a very smart autopilot. It studies weather, demand, prices, and equipment behavior, then constantly tweaks how the system runs so you get more clean energy for less money and wear-and-tear.