Neuroscience meets wave energy - boosting efficiency of AW-Energy’s WaveRoller

Neuroscience meets wave energy - boosting efficiency of AW-Energy’s WaveRoller

Feb 24, 2016
Renewables & smart grid
Energy production & distribution

Finnish wave energy manufacturer AW-Energy and Leibniz Institute for Neurobiology in Magdeburg, Germany will be studying neuroscience techniques in connection of boosting the efficiency of AW-Energy's WaveRoller® solution in Peniche, Portugal.

Techniques being developed through ground-breaking research in the field of neuroscience have great potential to boost the operation of wave energy converters. Technology harvesting energy from ocean waves is akin to the brain processing sound in that both are complex systems responding to incoming waves.

Thus, could models of brain function be combined with machine learning techniques to closely predict and optimize WaveRoller system performance? This question will be studied in a research project initiated between AW-Energy and Leibniz Institute for Neurobiology

Wave data, measured at one location, is essentially a one-dimensional time series, as is sound wave information entering the brain’s auditory system. This might make it possible to apply results from auditory neuroscience to work with wave data, mainly wave height and peak time. The research project sets out to develop an algorithm that learns from historical wave data and is constructed as neural networks mimicking the auditory system of the brain. This algorithm should be able to predict the incoming waves and the system response. In this case, the data will be collected from AW-Energy’s WaveRoller® installation site in Peniche, Portugal, but the algorithm could also be used at any installation site, learning the local wave dynamics.

In essence, the vision is to develop a control algorithm that will continuously adapt and optimize the WaveRoller® performance to any deployment site.

"With previous supervised training of the neural network and real time data coming in from several minutes before, it should be able to predict the system response with quite some accuracy," says Mercator Professor Patrick May at the Leibniz Institute for Neurobiology.

"In developing the WaveRoller technology we’ve been eager to learn from innovations made in other fields of science and engineering. We’re particularly excited about this initiative as it has the potential to bring significant benefits to both the power delivered and the availability of a wave farm installation," says Christopher Ridgewell, CTO at AW-Energy.

Discover what makes AW-Energy’s WaveRoller the proven highest quality in wave energy.

Image: AW-Energy

Source: AW-Energy

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