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

1–2% reduction in AEP uncertainty (P90 uplift) improving bankability and financing terms30–50% reduction in assessment cycle time through automated QC, data fusion, and rapid scenario iterationPortfolio-scale screening enabling developers to evaluate 5–10x more sites per analyst-year with consistent, auditable outputs

The Shift

Real-World Use Cases

AI Condition Monitoring for Wind Turbines

This AI framework monitors wind turbines to detect any problems early, helping to prevent energy losses.

anomaly detectionemerging

8.5

AI-Driven Predictive Maintenance for Wind Turbines

This AI system monitors wind turbines continuously to predict when they might fail, allowing for repairs before breakdowns happen.

anomaly detectiongrowing

8.0

Predictive Maintenance for Offshore Wind Turbines

This AI predicts when parts of wind turbines might fail, allowing for timely maintenance.