Artificial Intelligence Software

LOOP (Location Optimization for Offshore Platforms) Wind

Funding under the Blue Growth Programme


Machine learning for offshore wind development

Company Purpose

  • Our mission is to create and sell a Software to offshore wind promotors by Machine Learning.
  • This software will predict the best location for floating offshore wind platforms and will also change the design of any particular platform depending on the metoceanic conditions.

Problem & Solution


  • Climate Change is the result of Numen act and emissions.
  • Wind Energy is the fastest renewable energy growing sector (Cheapest LCOE), however wind onshore
  • Offshore wind promotors are facing big challenges to decrease their project coststo become competitive with onshore wind
  • High budget for DEVEX& CAPEX of new offshore Projects due to:
    • Buoys and testing
    • Expensive software to study the behaviour of floating platform under metoceanic conditions. Many hours/engineers.


  • 75% of EARTH is WATER
  • Offshore wind is exposed to stronger & steadier winds.Increasing 150% the onshore production.
  • Until late years, offshore was limited to shallow water zones, but floating will be the new market niche
  • We offer a FAST & CHEAPER solution to the floating offshore wind promotors, saving time, resources and money:
    • Reduced costs of testing and input of metoceanic data.
    • Gather many design and calculation softwares in JUST ONE.
    • Machine Learning will save many working hours of data accumulation, calculations and designing.

Value proposition

The technology is based in machine learning where algorithms train a computer to learn and make predictive analysis.

The model obtained for energy prediction gives a very reliable prediction of the energy output for newly supplied weather data and detects patterns

  • This concept has been developed rapidly for the last two years with the increase of data and computer processing power.
  • It can be applied to many industries worldwide.
  • Based on several research on wind in 2014, we can expect at least an optimization of the output of 5 %.
  • We can expect the neural networks to learn with the data and optimize the location of the multi turbines floating platform.