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Offshore wind energy and Machine learning

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Charley Rattan's picture
Hydrogen & Offshore Wind, business advisor and trainer Charley Rattan Associates

UK based offshore wind & hydrogen business advisor and trainer.Delivering global offshore wind business advice, problem solving and training:  www.charleyrattan.comCharley Rattan - offshore...

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This item is part of the Special Issue - 2019-10 - Artificial Intelligence, click here for more

Last week I was present at the founding of the ‘Offshore Energy Alliance.’ This group wishes to ensure that businesses come participate in Round 4 and other opportunities for offshore wind in the UK. The challenge is significant as the government has committed to 40GW total by 2030 which represents quadrupling of current operational capacity.

The meeting was well attended by businesses’ grateful for the opportunity of early engagement with an industry set upon a massive trajectory of significant sustained growth

The questions emerging from the floor were well- informed and showed a good knowledge of innovation, I was pleased stakeholders recognised that different opportunities presented for example by floating versus fixed foundation wind turbines.  Also asked of the panel was whether ‘machine learning’ is an innovation of interest to the offshore wind industry.

As offshore wind grows from the niche to the mainstream and gains further global traction, technologies already being trialled within the incumbent energy sector, will become relevant to the new offshore sector.   Machine learning, for example could inform costly maintenance strategies and programmes which, can be more smartly predicted as a result love educated and real-world data.  When combined with Smarter condition Monitoring Systems and Scada capabilities the potential is pretty obvious to insiders, such as me, engaged in tracking the industry and its future trajectory.

By way of answering the question the speaker indicated that the industry was indeed looking at machine learning and gave an example of high- definition aerial photography cited as an example Where people were involved accuracy was currently 30 to 40% higher than that derived from machine learning sources. The rest of the panel concurred and felt that although the gap was closing quickly but machine learning to be truly effective lay a year or two away.  

Businesses and especially stakeholders, acutely aware of potential job opportunities may of course be rather nervous of cost reduction exercises involving innovations such as machine learning

Currently the world leading offshore industry is indeed aware of the potential machine learning and its capabilities - stretching far beyond those mentioned   - but the offshore energy alliance at least believes its realization to be a couple of years distant.

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Matt Chester's picture
Matt Chester on Nov 29, 2019

By way of answering the question the speaker indicated that the industry was indeed looking at machine learning and gave an example of high- definition aerial photography cited as an example Where people were involved accuracy was currently 30 to 40% higher than that derived from machine learning sources. The rest of the panel concurred and felt that although the gap was closing quickly but machine learning to be truly effective lay a year or two away.  

A 30-40% difference definitely seems high at this time, but being 1-2 years away is really not that long for the tech to catchup. And given the slow pace the U.S. offshore wind industry is going, perhaps the silver lining is that by the time U.S. installations are going in we'll be able to integrate this rapidly improving type of solution!

Charley Rattan's picture
Charley Rattan on Nov 29, 2019

Indeed, the industry certainly expects machine learning to be sufficiently mature to be integrated into the UK's Round 4 schemes as they progress and currently at the bidding stage.

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