Wave Energy Forecasting

Uses AI to forecast wave conditions and optimize device control strategies to improve capture efficiency and survivability.

The Problem

“AI Wave Energy Forecasting and Control Optimization”

Organizations face these key challenges:

1

Wave conditions vary significantly by location and are hard to predict at device level

2

Fixed control strategies underperform across changing sea states

3

Extreme events are rare, so training and planning data is limited

4

Simulation models are computationally expensive and not always real-time ready

5

Sensor quality, drift, and offshore connectivity issues reduce data reliability

6

Operators need explainable recommendations for safety-critical decisions

7

Integrating metocean forecasts with device control systems is complex

Impact When Solved

Increase wave energy capture efficiency through adaptive controlReduce structural overload events and survivability risk during stormsImprove forecast accuracy for 5-minute to 72-hour planning horizonsLower O&M cost through earlier anomaly and load-risk detectionSupport emergency scenario simulation for rare high-impact eventsEnable better dispatch, maintenance scheduling, and grid integration

The Shift

Before AI~85% Manual

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.
With AI~75% Automated

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 it works

AI runs the first three steps autonomously.

Humans own every decision.

The system gets smarter each cycle.

Confidence90%
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

1 operating angles mapped

Operational Depth

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