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Wind Farm Site Selection

Machine learning for optimal wind farm location and layout optimization

The Problem

“AI Wind Farm Site Selection and Layout Optimization”

Organizations face these key challenges:

1

Wind resource quality varies significantly across small geographic areas and is hard to model accurately

2

Wake interactions between turbines create nonlinear energy losses that simple rules miss

3

Terrain, roughness, and offshore metocean conditions complicate micrositing decisions

4

Grid interconnection distance and capacity constraints can invalidate otherwise attractive sites

5

Environmental and permitting restrictions reduce usable land or sea area late in development

6

Remote and offshore maintenance logistics are expensive and often excluded from early site ranking

7

SCADA data quality issues and anomaly types distort operational assumptions used in planning

8

Failure modes such as yaw brake pad wear are not always directly monitored, creating hidden reliability risk

9

Engineering, GIS, finance, and operations teams often work in disconnected tools and data silos

10

Developers need explainable recommendations to support regulators, investors, and internal approval boards

Impact When Solved

Increase net annual energy production through optimized turbine placement and wake-aware layout designReduce lifecycle O&M cost by incorporating predictive maintenance and access constraints into site evaluationShorten site screening and feasibility analysis from months to daysImprove investment confidence with probabilistic yield, risk, and reliability scoringLower offshore service vessel and technician dispatch costs through advance repair scheduling inputsDetect hidden reliability risks such as yaw brake pad wear patterns before finalizing layout assumptionsImprove operational modeling by classifying SCADA anomalies before performance and degradation analysis

The Shift

Before AI~85% Manual

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
With AI~75% Automated

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 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.

Confidence96%
ArchetypeRecommend & Decide
Shape6-step converge
Human gates1
Autonomy
67%AI controls 4 of 6 steps

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.

Loop shapeconverge

Step 1

Assemble Context

Step 2

Analyze

Step 3

Recommend

Step 4

Human Decision

Step 5

Execute

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Technologies

Technologies commonly used in Wind Farm Site Selection implementations:

AI predictive maintenance modelsOther
4 mentions
Condition monitoring systemOther
4 mentions
IIoT sensorsOther
4 mentions
Remaining useful life predictionOther
3 mentions
Repair scheduling workflowOther
3 mentions

Key Players

Companies actively working on Wind Farm Site Selection solutions:

Condition monitoring vendors for wind turbinesindustrial IoT monitoring providerspredictive maintenance platforms

Real-World Use Cases

Wind turbine SCADA anomaly taxonomy and classification for operational context

Classify unusual turbine behavior into practical categories like downtime, curtailment, scattered bad readings, and high-wind derating so engineers know what kind of abnormal state they are seeing.

contextual and point anomaly classificationapplied research taxonomy embedded in a broader preprocessing workflow.
10.0

AI-assisted advance repair scheduling for wind turbines

Sensors watch wind turbines all the time, and AI looks for signs that parts are wearing out so operators can fix them before they break.

predictive analytics + early warning + remaining useful life estimationproposed/deployable condition-monitoring workflow described in a 2025 conference paper; credible but not evidenced here as a named commercial deployment.
10.0

Yaw brake wear prediction for offshore wind turbines using clustered controller data and LSTM

The system watches turbine controller signals to learn how yaw brake pads wear down, then estimates when they are likely to fail so operators can service them before a breakdown.

Time-series failure prediction with unsupervised data grouping as preprocessingreal-world implementation demonstrated on an operating offshore wind turbine component, but evidence is limited to a single component use case.
10.0

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