Wind Site Suitability Comparison
Compares HAWT and VAWT options for offshore wind site selection using resource assessment and corrected annual energy production estimates to reduce architecture and siting uncertainty.
The Problem
“Offshore wind developers lack a reliable way to compare HAWT and VAWT options at the same site before committing to architecture and layout decisions”
Organizations face these key challenges:
HAWT and VAWT options are often evaluated with different assumptions and tools
Offshore wind resource, metocean, bathymetry, and exclusion-zone data are fragmented across systems
Corrected AEP calculations require many loss assumptions that are hard to harmonize
Early-stage architecture decisions are sensitive to sparse or noisy site data
Impact When Solved
The Shift
Human Does
- •Gather offshore resource, metocean, bathymetry, and exclusion-zone inputs from separate sources
- •Apply architecture-specific assumptions and loss factors to HAWT and VAWT cases in separate analyses
- •Review consultant studies, spreadsheets, and GIS outputs to compare corrected AEP results
- •Decide which site-architecture combinations move forward for pre-FEED screening and detailed study
Automation
- •No AI-driven workflow in the legacy process
- •No automated cross-architecture scenario ranking
- •No consistent estimation of missing site parameters across datasets
- •No automated explanation of corrected AEP differences or siting risk drivers
Human Does
- •Set comparison assumptions, screening criteria, and acceptable uncertainty thresholds
- •Review ranked HAWT and VAWT site options and approve candidates for deeper engineering
- •Resolve exceptions where site data are sparse, conflicting, or outside normal operating assumptions
AI Handles
- •Ingest and harmonize offshore resource, metocean, bathymetry, and constraint data into a common comparison workflow
- •Generate side-by-side HAWT and VAWT scenarios using standardized assumptions and corrected AEP estimation
- •Estimate missing parameters, score feasibility, and rank site-architecture combinations by expected yield and risk
- •Highlight the main drivers of energy-yield differences and flag scenarios with elevated uncertainty or data-quality issues
Operating Intelligence
How it works
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 confirm a final offshore architecture or site recommendation without review and approval from the site-selection lead or investment decision-maker.[S1]
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
Technologies
Technologies commonly used in Wind Site Suitability Comparison implementations:
Key Players
Companies actively working on Wind Site Suitability Comparison solutions: