How Generative AI is Disrupting the Global Energy Sector

The world changed for many businesses with the arrival of machine learning, large language models, and natural language processing – in other words the advent of modern AI.

This technology is already emerging as a disruptive force in many industries around the world, and some are feeling the effects more quickly and more keenly than others.

Healthcare and education have been two early adopter industries. But the global energy sector – with its focus on analysis, innovation, and optimization – is one that stands to benefit most from the arrival of generative AI, with the potential to add billions of dollars in value to companies – if they deploy this technology correctly and effectively.

Data Analysis

The internet is awash with excited testimonials about how much this technology can do and how well it can do it. If you’re hoping to cut through the noise, MongoDB has a post on generative AI that explains what it can do and how it does it – but if you want the CliffsNotes, it’s really, really good at analyzing data, among other things.

This is a big deal for the energy sector because finding a competitive edge can come down to spotting marginal opportunities for optimization.

This might mean using generative AI to handle back-office tasks or using AI to analyze various business touchpoints for opportunities to improve. Spanish company Iberdrola has focused its digital strategy on analyzing complaints, deploying customer speech recognition, and developing robotic solutions.

Predictive maintenance

Keeping infrastructure working and in good condition is vital for a successful energy company. If a grid goes down or a pipeline springs a leak, it can mean millions of angry customers and a PR nightmare.

But with infrastructure spread across the global and often into very remote areas, it can be a challenge to stay on top of this.

Many energy companies are turning to generative AI for predictive maintenance solutions. ThinkGeoEnergy has reported on Italian company’s Enel using predictive maintenance applications at over 16,000 substations, using sensors to compile data which is combined with historic information and analyzed by systems powered by machine learning.

Optimised consumption

Effectively managing power grids can be tricky when everyone needs energy and most of them need it at roughly the same time. This places stress on the grid at certain times.

Energy companies manage this by deploying Demand Response Management, essentially encouraging some customers – often commercial or industrial customers – to use energy at times when the grid is otherwise relatively quiet.

AI can help with this by creating an interactive link between providers and consumers, allowing for real-time responses to shifts in demand and even some time predicting those shifts before they happen.

Energy Forecasting

Renewable energy is becoming a more and more valuable aspect of the energy sector, and a few years ago the renewable energy industry globally was valued at nearly $900bn.

As renewable energy becomes an increasingly vital part of the global energy industry, the ability to understand and predict the forces that generate those types of energy becomes ever more valuable. But that’s not a simple task.

Predicting the weather is famously difficult. And yet The World Economic Forum has documented how AI can outperform conventional weather forecasting in a fraction of the time. Algorithms powered by AI can analyze weather conditions, whether that means changes in wind or solar exposure and combine historical data with real-time information.

This enables providers to effectively forecast how much renewable energy is likely to be flowing on to the grid, and how to manage the demand for it.