How AI is accelerating the transition to renewable energy
Offshore wind power has fast become one of the most promising renewable sources of energy. Its growth is expected to continue, with generation capacity predicted to soar from 35GW to 234GW over the next 10 years, according to the Global Wind Energy Council (GWEC), which ranks the UK, Germany, and China as the largest national markets.
The sector is a particular focus in governments’ energy strategies, given the plummeting costs and the fact turbines can now be placed ever further from coastlines. Boris Johnson has even stated that he wants the UK to become the ‘Saudi Arabia of wind power’. The GWEC predicts significant growth over the next five years, with an estimated compound annual growth rate of nearly 32 percent, compared to just 0.3 percent with land-based turbines.
The transition away from fossil fuels and towards cleaner renewable sources of energy is critical to the decarbonization agenda and efforts to prevent runaway global warming. However, while that is widely accepted, few realize that the energy transition business also has areas that need to be carefully managed to ensure they don’t have a negative environmental impact. In much the same way as energy companies operate and maintain oil and gas subsea assets, wind farm cables, foundations, and all other components of the turbines also need continuous monitoring and maintenance.
In order to access, monitor, and maintain offshore wind farms, cables, and pipes, energy companies send out large vessels that use vast quantities of fuel, are incredibly expensive to run, and are often crewed by up to 60 people, from engineers and robot drivers to cooks and cleaners. The vessels, which cost £1m to £10m a month to operate, depending on the job, will emit 275,000 tonnes of carbon over their lifetimes.
The work is necessary and unavoidable, however innovative technologies are enabling energy companies to improve their processes and reduce the burden on the environment. By leveraging sophisticated robotics, AI, machine learning, and autonomy energy companies can significantly reduce their environmental impact and make important strides to become more sustainable.
It’s in the area of sustainability that AI may help reap the best rewards. Marine robotics using autonomy and machine learning technologies are pivotal in improving efficiencies and easing the transition to renewable energy sources, and ways of working in offshore environments.
While the energy industry is a relatively early adopter of robotics, the use of more advanced technologies, such as simultaneous localization and mapping (SLAM), machine learning, and increasingly autonomous ROVs (Remotely Operated Vehicles), presents an opportunity that too few are seizing.
Seabed surveys, used to maintain wind farm assets below the surface, are carried out from vessels deploying sonars that map the seabed. For closer inspections, the majority of companies are using manually operated ROVs collecting video data. With each ROV needing at least two pilots to operate it. And then the data collected is inspected manually by an additional team.
