Wind Resource Visibility
AI solution for wind turbine health monitoring and wind market intelligence, combining operational analytics with API-fed market data workflows to support asset performance, planning, and investment decisions.
The Problem
“Wind asset and market decisions are slowed by fragmented operational and commercial data”
Organizations face these key challenges:
SCADA, maintenance, weather, and market data live in separate systems
Manual alarm review creates delayed response to emerging turbine issues
Commercial and technical teams use inconsistent assumptions and models
Large external market datasets are difficult to ingest into internal workflows
Impact When Solved
The Shift
Human Does
- •Export and reconcile SCADA, maintenance, weather, and market data from separate sources
- •Review turbine alarms and performance trends to identify likely issues
- •Build planning, due diligence, and investment scenarios using spreadsheets and ad hoc models
- •Compare technical and commercial assumptions across teams and align on inputs
Automation
- •Provide basic threshold alerts from existing monitoring tools
- •Refresh dashboard views from scheduled data loads
- •Surface historical KPI trends for manual review
Human Does
- •Approve maintenance actions and outage priorities for flagged turbines
- •Review AI-generated market and asset scenarios and choose planning assumptions
- •Handle exceptions where data quality, unusual operating conditions, or market shifts require judgment
AI Handles
- •Continuously monitor turbine behavior and detect anomalies or emerging failure risk
- •Fuse operational, weather, and API-fed market data into standardized analytics views
- •Prioritize assets and maintenance actions using predicted risk and asset criticality
- •Generate planning, due diligence, and investment scenarios with ranked tradeoffs
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
WindSight must not approve maintenance actions or outage priorities for flagged turbines without review by an asset manager or maintenance planner [S1][S2].
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 Resource Visibility implementations:
Key Players
Companies actively working on Wind Resource Visibility solutions:
Real-World Use Cases
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