How are Machine Learning and Artificial Intelligence (AI) Reshaping the Energy Industry in Europe?
DNV GL operates globally across a range of industries to provide trust and help stakeholders understand and manage risks. The company has over 2,200 experts providing advice for renewable generation, transmission, distribution and energy management and efficiency. The work they do ranges from testing high voltage grid components to bankability assessments to allow solar farms to be financed. Their latest research showcases the insight related to how drone technology and computer vision can help with inspections while also providing a global outlook to 2050.
We wanted to learn more about the energy innovations DNV GL is exploring and utilizing, so we talked with Elizabeth Traiger, Senior Researcher, Power & Renewables DK & GB at DNV GL – Energy. We talked with her about the biggest issues in the energy industry, what some of the most notable takeaways were from her research, how DNV GL has measured the difference the technology makes and much more.
Jeremiah Karpowicz: In what ways have you seen digitalization reshape the energy industry? Is that more in terms of logistics or expectations?
Elizabeth Traiger: The energy industry is undergoing rapid change and in our Energy Transition Outlook (dnvgl.com/eto) we forecast a major shift away from fossil fuels, towards renewable energy. By 2050 up to 70% of the world’s electricity supply will be provided by renewable energy. At the same time, the way that we use electricity is changing; consumers are now also generators with a proliferation of rooftop PV and transport is being electrified with a huge uptake in electric vehicles forecast in the coming years. These major trends of decarbonisation and decentralisation are being enabled by digitalisation. It is digital technology which is allowing this reshaping of the energy industry.
What are some of the biggest issues in the energy industry right now? Do those issues vary from region to region?
Issues surrounding the variability of renewables increasing the proportion of energy sources on the grid and reducing the levelized cost of energy are common in all regions. Digitization, AI and the use of drones help renewables to reduce uncertainty in energy production estimates and the condition of the renewable assets. This allows for grid stability as a greater proportion of energy the energy mix comes from non-fossil generation. In addition, reductions of uncertainly help to get renewables closer to grid parity by opening financing options.
What are some of the practical applications of computer vision?
For energy facilities, practical applications range across the entire lifetime chain: development, operations and decommissioning. In development, computer vision (CV) is used for environmental and land surveys. During construction, CV is used to monitor progress, and identify any issues with installation. Operations utilize CV to gain insights from drone inspection imagery. CV helps to assess any damage on the plants and aid in determining accurate power production estimates, for example using thermal imagery for solar facilities. Also, CV is used for casting- analysing satellite imagery for cloud coverage which impacts solar facilities, or to predict large storms events that impact any maintenance projects, hinder energy production and have protentional for damage to the projects and connecting infrastructure. In decommissioning, CV is used to maintain accountability and ensure remote sites in hard to access areas are returned to appropriate undisturbed levels for the environment.
