AI Wind Resource Assessment
Machine learning for wind resource characterization and prediction
The Problem
“Reduce wind uncertainty to de-risk project finance”
Organizations face these key challenges:
High uncertainty in long-term wind and wake losses due to limited on-site measurement periods and complex terrain/atmospheric stability
Slow, expert-dependent workflows (MCP, mesoscale/CFD downscaling, QC) that are difficult to scale across multi-site pipelines
Inconsistent results and rework during lender independent engineer reviews, driven by subjective assumptions, data gaps, and model bias
Impact When Solved
The Shift
Human Does
- •Plan measurement campaigns and select reference datasets for each site
- •Review, clean, and filter met mast, LiDAR, and power data
- •Run MCP, mesoscale, and terrain correction workflows with expert judgment
- •Estimate AEP, losses, and uncertainty assumptions for investment cases
Automation
- •Apply basic automated data checks and flag missing or inconsistent records
- •Generate standard reports and calculation summaries from analyst inputs
- •Store historical measurement and assessment data for reuse
Human Does
- •Approve site assessment scope, measurement strategy, and bankability criteria
- •Review AI-generated AEP, uncertainty, and risk scenarios for investment decisions
- •Resolve flagged data gaps, unusual terrain effects, and model exceptions
AI Handles
- •Fuse measurement, reanalysis, mesoscale, terrain, and operational data into site wind resource assessments
- •Automate data quality control, anomaly detection, and reference data screening
- •Generate long-term wind, shear, turbulence, and energy yield predictions with calibrated uncertainty
- •Run rapid scenario comparisons across sites, layouts, and assumptions
Operating Intelligence
How AI Wind Resource Assessment runs once it is live
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 finalize bankability assumptions or approve a site assessment for financing use without review by the wind resource assessment lead or designated decision-maker. [S2] [S4]
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
Real-World Use Cases
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AI-driven early warning condition monitoring for wind turbine subassemblies
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Yaw brake pad failure prediction for offshore wind turbines
The system watches turbine sensor data over time and estimates when yaw brake pads are likely to wear out, so crews can fix them before they fail.