Wind Terrain Resource Insight
An AI-powered wind resource and site assessment application for wind farm development, combining terrain-aware layout analysis, local climate modeling, and lidar-based high-altitude measurement workflows to reduce uncertainty, cost, and planning risk in complex terrain.
The Problem
“Reduce wind farm development uncertainty in complex terrain with AI-driven resource assessment”
Organizations face these key challenges:
Complex terrain distorts wind flow and makes simple extrapolation unreliable
Traditional met mast deployment is expensive, slow, and operationally risky
Modern turbine hub heights exceed many conventional measurement setups
Wind resource, terrain, and layout analyses are often performed in disconnected tools
Impact When Solved
The Shift
Human Does
- •Review candidate sites using manual GIS studies and terrain maps
- •Plan met mast or lidar measurement campaigns and coordinate field deployment
- •Compare turbine layout options with consultant-led micrositing and spreadsheets
- •Compile wind resource evidence and uncertainty assumptions for planning and financing
Automation
Human Does
- •Approve which candidate sites and layouts move into detailed development
- •Review AI-generated resource estimates, uncertainty ranges, and bankability conclusions
- •Decide when additional field measurement or expert study is required for exceptions
AI Handles
- •Score and rank candidate sites using terrain, wind climate, land constraints, and turbine fit
- •Analyze layout scenarios with terrain-aware wind resource and wake-loss estimates
- •Process lidar measurements with automated quality checks, gap-filling, and hub-height wind estimation
- •Generate consolidated site assessment outputs with scenario comparisons and uncertainty summaries
Operating Intelligence
How it works
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not approve a site, turbine layout, or development pathway without a wind development manager's decision. [S1][S2]
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Wind Terrain Resource Insight implementations:
Key Players
Companies actively working on Wind Terrain Resource Insight solutions:
Real-World Use Cases
Terrain- and climate-aware wind farm layout/resource analysis
The workflow uses maps, wind patterns, and wind farm settings to study how a planned wind farm might perform at a specific site.
Lidar-only wind resource assessment for Cairn Duhie Wind Farm
Instead of building a tall measurement mast, the project used ground-based lidar lasers to measure wind across turbine heights and plan the wind farm.
High-altitude wind measurement for wind resource assessment in complex terrain
Use specialized wind-sensing equipment to measure wind higher up in the atmosphere, including in difficult terrain, so wind developers can better estimate how much energy a site could produce.