The Future of AI in Renewable Energy

Over the last few years, the use of generative AI has grown rapidly, bringing with it both exciting opportunities and important challenges.

In the renewable energy sector, AI offers the potential to lower costs, improve efficiency, and speed up innovation, helping the world transition to cleaner power faster. At the same time, adopting these tools requires careful consideration of risks, such as high energy consumption, reliance on complex systems, and ethical questions around data use.

As the sector continues to evolve, the key question becomes: how can we harness the benefits of AI while managing its costs to ensure a more sustainable energy future?

Increased Efficiency

In the past, new energy technologies have taken decades to move from idea to everyday use. For example, solar panels and electric cars took more than 30 years to reach wide adoption.

AI has the potential to cut this time dramatically. By processing huge amounts of data quickly, it can help scientists and engineers make discoveries faster, design better technology, and bring solutions to market sooner. Recent history shows how innovation transforms the sector: unconventional oil and gas, solar PV, and electric vehicles all saw dramatic growth between 2010 and 2024.

Future Energy Systems

As the renewables sector grows, there is an increased need for tools that can handle large, dynamic datasets and make real-time decisions.

Advances in AI, including machine learning and self-modifying algorithms, offer powerful capabilities for analysing data, forecasting, optimising operations, and automating control in energy systems.  By bridging complexity and control, AI seems set to underpin the transition toward cleaner, smarter, and more reliable energy systems.

Environmental Impact

The flip side of this argument is a large one. The environmental impact of the scale of AI usage cannot be ignored. The data centres that support AI create significant electronic waste, rely on massive quantities of water for cooling, and often depend on rare or unsustainably sourced minerals. 

They also burn huge amounts of electricity, much of which still comes from fossil fuels, contributing to greenhouse gas emissions. According to UNEP, these effects are compounding, and unless AI deployments are designed with environmental safeguards, the harm could outweigh the benefits.

What’s Next?

Does the benefit of increased efficiency moving the world towards a green energy transition worth the environmental cost of running the technology?

If AI technology is found to be detrimental for the environment, is it too late to change course, or can innovation, regulation, and a stronger commitment to renewable energy steer AI toward becoming part of the solution rather than part of the problem?