In the position paper “Making Renewables Smarter: The benefits, risks, and future of artificial intelligence in solar and wind”, DNV GL explores where artificial intelligence (AI) will have an impact to increase efficiencies in the renewables industry.
AI is expected to automate operation over the course of the following years in the solar and wind industries and boost efficiencies, as well, across the renewable energy sector, DNV GL mentiones.
Wind and solar generation plants have beneﬁted from the recent development of these technologies and have had sensor technology installed from the beginning.
As a result, most of the advances supported by artificial intelligence have been in resource forecasting, control and predictive maintenance.
“We expect the installation of more sensors, the increase in easier-to-use machine learning tools, and the continuous expansion of data monitoring, processing and analytics capabilities to create new operating efficiencies—and new and disruptive business models,” stated Lucy Craig, Director Technology and Innovation at DNV GL – Energy.
Solar and wind industry stakeholders will see artificial intelligence benefits in a number of areas, including:
Robotics growing in prevalence for remote inspection, with new benefits in maintenance and troubleshooting.
Crawling robots that can get close to a structure’s surface enabling a new set of technologies.
Supply chain optimizations by autonomous driving robots, which can in future build entire onshore wind or solar farms.
Autonomous drones with real-time artificial intelligence-supported analysis will become the primary tool for carrying out effective and efficient inspections of wind turbines and solar panels.
AI applications accelerating due diligence, reducing the time investment of planning and analysis that today requires many human hours.
Artificial intelligence will also automate decision making, driving costs out of energy development, production, and delivery in the solar and wind industries.
For most operators in the renewables industry, building artificial intelligence systems that are stable, progressive and reliable requires sets of knowledge and data from across many different projects. To achieve that, a deep knowledge of the domain of industry experience is key.
Fortunately, there is a large amount of historical data in the market, DNV GL concludes.