AI Wave Energy Forecasting
Uses AI to forecast wave conditions and optimize device control strategies to improve capture efficiency and survivability.
The Problem
“Uncertain wave power output disrupts grid planning”
Organizations face these key challenges:
High forecast uncertainty from rapidly changing sea-states leads to conservative bidding, imbalance penalties, and reduced merchant revenue
Limited visibility into site-specific wave transformation and device-level performance causes systematic bias in power estimates
Inefficient maintenance and marine operations planning increases vessel days, weather abort rates, and unplanned downtime
Impact When Solved
The Shift
Human Does
- •Review regional marine forecasts, buoy readings, and recent SCADA trends to estimate expected wave conditions and power output.
- •Set day-ahead bids, dispatch expectations, and operating limits using rule-of-thumb power matrices and conservative safety buffers.
- •Plan maintenance windows and vessel mobilization based on manual weather interpretation and asset availability.
- •Investigate unexpected production shortfalls or shutdowns by manually comparing sea-state conditions with device performance.
Automation
- •No meaningful AI support in the legacy workflow.
- •At most, provide basic historical averaging or static rule-based output estimates.
- •Surface standard forecast feeds and raw monitoring data without site-specific learning.
Human Does
- •Approve bidding, dispatch, and curtailment decisions using AI forecast ranges and recommended operating guidance.
- •Decide maintenance timing, vessel deployment, and safety actions based on forecast confidence and operational priorities.
- •Review and resolve forecast exceptions, anomaly alerts, and suspected sensor or device performance issues.
AI Handles
- •Generate site-specific probabilistic wave and power forecasts across intraday and day-ahead horizons.
- •Continuously fuse marine conditions, telemetry, and availability data to update expected output and operating envelopes.
- •Recommend optimal control windows, dispatch alignment, and maintenance windows based on forecasted sea-state and uncertainty.
- •Monitor forecast error, detect sensor drift or performance degradation, and triage conditions likely to cause curtailment or downtime.
Operating Intelligence
How AI Wave Energy Forecasting runs once it is live
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 place bids, commit dispatch positions, or approve curtailment without a human decision-maker reviewing the forecast range and uncertainty. [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 AI Wave Energy Forecasting implementations:
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