AI Wind Farm Site Selection
Machine learning for optimal wind farm location and layout optimization
The Problem
“Optimize wind farm siting amid complex constraints”
Organizations face these key challenges:
Fragmented data and inconsistent quality across wind resource, environmental constraints, land rights, and grid deliverability make apples-to-apples site comparison difficult
High uncertainty in long-term yield and curtailment (wake, icing, congestion, and grid outages) leads to conservative financing assumptions and lower project value
Permitting and interconnection risk is often discovered late, causing costly rework, queue restarts, or project cancellation
Impact When Solved
The Shift
Human Does
- •Compile wind, land, setback, habitat, and grid data to screen candidate parcels
- •Apply exclusion rules and expert judgment to shortlist sites for deeper study
- •Review met mast results, desktop yield studies, and layout options to select preferred sites
- •Assess permitting and interconnection feasibility with consultants and utility queue information
Automation
- •No material AI support in the legacy workflow
- •Basic GIS overlays and spreadsheet calculations summarize site constraints
- •Simple scenario comparisons estimate energy yield and project economics
Human Does
- •Set development priorities, risk tolerance, and approval criteria for site selection
- •Review AI-ranked parcels and approve which sites advance to field studies and land outreach
- •Decide how to handle flagged permitting, interconnection, or stakeholder exceptions
AI Handles
- •Aggregate and score candidate parcels on wind yield, wake losses, curtailment, cost, and constraints
- •Rank sites by expected value and uncertainty, including P50 and P90 energy outcomes
- •Generate optimized layout scenarios and compare land use, AEP, and revenue trade-offs
- •Flag high-risk permitting and interconnection pathways for early triage and rework avoidance
Operating Intelligence
How AI Wind Farm Site Selection 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 advance a site to field studies or land outreach without approval from the development director or designated site selection lead. [S1][S3]
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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