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Felicity Bradstock

Felicity Bradstock

Felicity Bradstock is a freelance writer specialising in Energy and Finance. She has a Master’s in International Development from the University of Birmingham, UK.

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Artificial Intelligence Will Be Critical For Renewable Energy Growth

  • Artificial intelligence has been playing an ever-larger role in the global energy industry, especially as the oil and gas industry has gone digital in recent years.
  • As the global energy transition takes place, the importance of AI in grid management will grow as it will be increasingly vital to have accurate energy consumption estimates.
  • Both solar and wind energy can be improved by the use of AI during the construction and production phases of projects.
Solar panels

The digitalization of oil and gas has been well documented, with pretty much all energy majors adopting AI, machine learning, and other innovative technologies to improve their operations. But what role does artificial intelligence play in renewables? Just as in oil and gas, AI is being adopted for use in wind, solar, and other green energy projects to improve efficiency through greater automation. As energy firms look to digitalize their operations to a greater extent, AI will likely play a leading role in the energy transition of the future.  The use of AI can support numerous activities across the energy industry, for operations across all energy sources, from fossil fuels to renewables. The energy industry has adopted AI technology in recent years to support automated decision-making and aided decision-making. The first is when computer systems process information autonomously, without human input. This often means that tasks can be completed faster and more efficiently than when a human decision is required, as the machine can make an immediate change. However, some issues require greater human input to determine the correct response; in this case, aided decision-making can be useful. Machines can provide useful insights by providing data for workers to interpret and decide on the right actions to take in any given situation. 

AI also plays a major role in prediction. The use of complex algorithms can help investors to determine the level of risk involved in a new green energy project, anticipate the energy production from different types of renewable sources in different conditions, and predict the energy demand at different times of the day in various locations. Technology providing constant monitoring and evaluation can help companies prevent failures or the need to halt operations, by anticipating potential challenges and responding to them immediately. For example, using machine learning weather models, historical datasets, and real-time local weather information can help companies to predict when a storm or heatwave is going to hit to adapt their operations to prepare for the change in weather. Related: Black Sea Shipping Rates Jump 20%

With digitalization becoming commonplace, energy firms are now using AI technologies in their day-to-day operations, and this type of equipment will almost certainly be key to transforming the future of energy. One of the main ways in which AI will support an effective transition away from fossil fuels to greener alternatives is through grid management. AI and machine learning use data analytics to estimate the level of energy consumption across households in any given area. It considers a variety of factors such as time of year, peak and off-peak times, and weather conditions. This can help energy companies to be constantly aware of the likely electricity use in the coming days, manage the grid accordingly and avoid outages. Production can also be altered in response to usage predictions to meet demand and avoid waste.

The rollout of AI technology across different areas of energy operations can also significantly enhance maintenance practices. Machines can predict the need for maintenance to schedule a repair ahead of an outage, to avoid an unnecessary loss of power. Energy companies can prepare for maintenance and inform consumers, rather than be caught unexpectedly by something breaking, which would mean longer repair times and unexpected power cuts for customers. 

When it comes to solar power, AI can be used to determine the best sites to construct solar farms, based on the hours of sunlight and intensity. It can also help operators to plan the layout of the site so that solar systems catch the most sunlight. Once operational, AI technology can be used for automated decision-making to control solar panels as they rotate toward the sunlight throughout the day. 

Even J. Kvelland, the co-founder and COO of solar AI company Glint Solar, explained: “To us, it’s surprising how many very sophisticated solar developers are still using the old way of sourcing land: reactively waiting for someone to recommend a piece of land or guessing by looking at Google Earth.” He added, “Given how ambitious plans virtually all developers have, they increasingly must be proactive about site screening and we’re proud to finally offer them software for this important task.”

In terms of wind power, Danish renewable energy major Vestas Wind Systems has led the way in the digitalization of wind farms, using machine learning to constantly adapt and improve operations. On-site AI technology learns from the environment in real-time, mainly through trial and error, to create changes to enhance wind energy production. 

Espen Mehlum, the head of the energy and materials program on benchmarking at the World Economic Forum stated, “You can use AI to both optimize the construction, siting and the operations of a wind farm, but more importantly, you can use AI to optimize across different systems, both when it comes to consumption but also production.” He added, “That’s where the huge untapped potential is for AI – we’re just scratching the surface and seeing the first use cases.”

The digitalization of the energy sector is well underway, with almost all oil and gas and renewables majors incorporating a wide range of innovative technologies into their operations, for greater efficiency and production stability. AI technologies allow energy companies to predict a range of scenarios, ensure a reliable energy output for consumers, support grid efficiency, and adapt to anticipated and real-time changes to establish optimal conditions for production.

By Felicity Bradstock for Oilprice.com

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