AI Agrivoltaics Optimization
Machine learning for dual-use solar and agriculture systems
The Problem
“Optimize agrivoltaics yield, revenue, and land use”
Organizations face these key challenges:
Design trade-offs are complex: small changes in GCR, tracker height, and row spacing can materially impact both capacity factor and crop outcomes, but interactions are hard to quantify with traditional tools.
Limited, noisy data across seasons makes it difficult to forecast crop yield and PV performance together, increasing financing uncertainty and risking missed P50/P90 targets.
Operations are siloed: solar dispatch/tracking and farm management (irrigation, planting, harvesting) are rarely coordinated, causing avoidable curtailment, water waste, and crop stress.
Impact When Solved
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 for Energy Systems
Think of this as a playbook of AI tricks for running power systems—generation, grids, and consumption—more like a smart thermostat and less like a manual on/off switch. It applies machine learning to decide how much power to produce, when to store it, and how to route it so the overall system is cheaper, cleaner, and more reliable.