AI Solar Farm Design

AI-optimized solar farm layout, tracking, and design

The Problem

“Optimizing solar farm design under complex constraints”

Organizations face these key challenges:

Slow, manual iteration across GIS/CAD/energy models limits scenario exploration and forces conservative designs

Late discovery of constraints (setbacks, wetlands, slope limits, interconnection caps, constructability) drives costly redesign and schedule delays

Fragmented handoffs between development, engineering, and EPC teams create inconsistent assumptions and rework in grading, electrical routing, and yield estimates

Impact When Solved

1–3% higher net AEP through optimized row spacing, shading, and DC/AC configuration2–5% lower BOS/civil CAPEX via optimized grading, pile selection, and cable/road routing30–60% faster design-to-permit iterations, reducing rework and cutting weeks off the path to NTP/COD

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.

Time-SeriesEmerging Standard

8.5

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.