Wind Resource Assessment

Machine learning for wind resource characterization and prediction

The Problem

“AI Wind Resource Assessment and Turbine Reliability Intelligence”

Organizations face these key challenges:

Yaw brake pad degradation is often not directly monitored and is discovered late

SCADA data contains mixed anomaly types that distort performance analysis

Remote and offshore maintenance visits are expensive and difficult to schedule

Wind conditions are highly variable across terrain, season, and turbine position

Threshold-based alarms generate false positives and miss early-stage failures

Maintenance, weather, and operational data are siloed across systems

Operators lack consistent remaining useful life estimates for planning repairs

Impact When Solved

Earlier detection of yaw brake pad wear before secondary damage occursCleaner SCADA datasets through anomaly taxonomy and contextual classificationImproved wind resource and power production forecasting accuracyReduced unplanned turbine downtime and emergency maintenance eventsBetter repair scheduling for offshore logistics, vessels, and spare partsHigher fleet availability and more reliable revenue forecasting

The Shift

Before AI~85% Manual

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

With AI~75% Automated

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 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.

Confidence92%

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

1AI

Assemble Context

Combine the relevant records, signals, and constraints.

instant

2AI

Analyze

Evaluate options, risk, and likely outcomes.

instant

3AI

Recommend

Present a ranked recommendation with supporting rationale.

instant

4Human checkpoint

Human Decision

A human accepts, edits, or rejects the recommendation.

hours to days

Authority gates · 1

The system must not finalize bankability assumptions, lender responses, or investment-facing energy yield cases without approval from the responsible assessment lead or decision owner [S3][S5].

Why this step is human

The decision carries real-world consequences that require professional judgment and accountability.

5AI

Execute

Carry out the approved action in the operating workflow.

instant

6Feedback

Feedback

Outcome data improves future recommendations.

continuous

1 operating angles mapped

Operational Depth

Technologies

Technologies commonly used in Wind Resource Assessment implementations:

Turbine controller sensor dataOther

4 mentions

Contextual anomaly detectionOther

2 mentions

Point anomaly detectionOther

2 mentions

Key Players

Companies actively working on Wind Resource Assessment solutions:

OEM predictive maintenance systems other ML approaches for wind turbine fault detection such as vibration monitoring and gearbox fault models condition-based maintenance platforms for wind turbines

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