Winds of Change: How the Renewables Revolution is Harnessing AI

Artificial intelligence (AI) and renewable energy have quietly established a symbiotic relationship, each helping to increase the other’s viability and opening the door to a whole range of new possibilities.  

AI’s predictive qualities are helping to boost energy efficiency, predicting power usage trends and making it simpler than ever to integrate renewable energy into the grid. It’s also supporting existing renewable technologies to be more reliable than ever.  

While the energy consumption of training and operating large language models (LLMs) has been flagged as excessive, European and UK directives set to invest in green data centres are promising to reduce the environmental impact of training and running AI models, as well as maintaining equipment and cooling servers.    

Predicting energy demand peaks and troughs 

“If you’re anything like me,” says Enright, “you’ll often pop the kettle on for a cuppa during an ad break when watching terrestrial TV – and even while streaming, thanks to the introduction of ads on popular streaming services.” It’s well-known that this has placed strain on the power grid in the past, but now thanks to AI, we can predict more than just a quick cuppa break when we’re waiting for Coronation Street to come back on. 

By analysing vast swathes of power usage data, AI can help the power grid manage demand better by understanding when we’re using energy the most. That same principle applies to renewable energy too: these AI-powered systems can understand when renewable energy is available and when it’s required.  

This also makes integrating renewable energy into the grid simpler. Predicting wind power can help us to understand how much energy can be collected by turbines, which can in turn forecast how much of it will be available to the grid. 

Karen Panetta, an Institute of Electrical and Electronics Engineers’ fellow, adds: “[AI is used to] correlate trends and do better forecasting. AI can allow us to explore relationships and look at ways to mitigate failures in the grid and understand how to re-distribute energy in the most efficient ways.” 

Keeping energy generators up and running 

Renewable energy generators, like wind turbines and solar panels, are not immune from wear and tear and the need for maintenance. But rather than waiting for a fault to occur to fix generators, businesses are using AI for predictive maintenance. 

This involves using sensors placed on the generators, which will analyse data and predict when they’ll need maintenance performed. “Considering how many of these generators – particularly wind turbines – are placed in remote locations,” Enright comments, “this allows for the strategic scheduling of maintenance to minimise downtime.”  

As well as predicting the maintenance of generators like wind turbines, AI can also be harnessed to monitor temperature and identify hot spots on large-scale solar panels, which can indicate malfunctioning cells. Maintenance can be performed on the panels, but in the meantime, they can be re-angled to optimise the power captured. 

Simulating and predicting weather conditions 

Another of AI’s many renewable applications lies in its ability to predict – and then simulate – future weather conditions.  

Enright says: “Renewable energy will always be available in the sense that there will always be sun, wind, organic material and rain. The unpredictability comes in that it’s not always sunny, rainy or windy – and too much or a lack thereof these conditions can then affect organic materials like the growth of grass and plants.” 

“Intelligent weather simulators are being used to predict future weather conditions, giving us insight into our future energy capture potential. But these tools are used in a way that far outstrips simple weather reports; one simulator shows how the layout of a city can impact airflow.  

“This means that architects can support the future of renewable energy by using this insight to design buildings and cities that work with the weather and renewable energy sources, not against them,” she adds.  

Making generators more sustainable 

The production of renewable energy supports the fight against climate change, so it must be fully sustainable, right? Well, not necessarily. 

Many renewable energy generators are made from rare earth metals, using valuable and limited resources. As well as the materials themselves, the process of manufacturing these generators can be highly energy-intensive. 

“AI is being used to speed up trials of new materials and their performance,” Enright adds, “meaning thousands of manual tests can be condensed into a more manageable number. What’s more, AI can support in making sure that these generators are recyclable once they reach their end of life, a key tenet of sustainability.” 

The renewable energy sector is one of many that is benefiting from the transformative effects of AI. From ensuring generator uptime is maximised to predicting energy demand and adapting accordingly, we’re seeing this smart technology improve our generation and usage of renewable energy. And considering the importance of the fight against climate change, this may be one of its most important uses to date.