AI Tidal Energy Optimization

Applies AI to predict tidal resource variability and optimize turbine control and maintenance for higher energy yield.

The Problem

“Maximize tidal output while minimizing O&M costs”

Organizations face these key challenges:

Highly variable tidal currents and turbulence drive non-linear power and fatigue loads, making fixed operating envelopes overly conservative or risky

Unplanned failures are expensive due to limited weather windows, specialized vessels, and long lead times for subsea interventions

Data is siloed across SCADA, condition monitoring, metocean sensors, and grid signals, limiting actionable insight and increasing decision latency

Impact When Solved

Increase availability by 2–5 percentage points through earlier fault detection and optimized maintenance timingReduce major component replacements (e.g., bearings, seals, gearboxes) by 10–20% via load-aware control and degradation monitoringLower vessel mobilizations by 15–30% through risk-based maintenance planning aligned with metocean windows

Real-World Use Cases

Artificial Intelligence in Renewable Energy Optimization

This is like giving a wind farm or solar plant a very smart autopilot. It studies weather, demand, prices, and equipment behavior, then constantly tweaks how the system runs so you get more clean energy for less money and wear-and-tear.

Time-SeriesEmerging Standard

8.5

AI-Driven Energy Flexibility Optimization

This is like giving the power grid a smart autopilot that learns when to turn power plants, batteries, and big industrial loads up or down so you always have enough electricity at the lowest cost, with fewer blackouts and lower emissions.