The role of AI in electrification: increasing the adoption of renewables

The panel began with a brief introduction to the concept of electrification and the need to transition from fossil fuels to electrical alternatives. This new wave of electrification includes renewable energy sources such as wind farms, solar panels, and heat pumps.

Accelerating the adoption of renewables

Crystal Hirschorn, CTO at Zoa, emphasised the need to harness technology to drive the transition towards net zero. She explained that while there is a lot of talk about AI and data science, there is a need to connect this with effecting a change in consumer behaviour and thereby influence policy decisions. By way of an example she explained how Zoa, using machine learning and leveraging billions of data points, has gained insights into customer behaviour and built new models that optimise energy assets within the home. This allows Zoa to demonstrate the value return to the customer on their initial investment in a given renewable asset, helping to validate and increase adoption.

Guilherme outlined his view that AI’s core role in the transition to electrification is enabling people to make better decisions. For instance, he explained how AI could play a key role in managing the anticipated network load as a result of the adoption of EV’s, by helping consumers to decide when best to charge their vehicle using data from the grid. He went on to elaborate that the key bottleneck in the energy transition remains the grid itself, with the nature of decentralised renewable sources making it increasingly difficult to guarantee supply. He highlighted AI’s ability to help with demand side flexibility as an aid to solving this challenge.

Addressing vulnerabilities and social equity

Angela Bradbury from Help First discussed how they are using AI to help Citizens Advice Bureau to prioritise vulnerable customers, ensuring that those at risk of energy cutoffs receive timely support. Using large language models (LLMs), they can summarise case notes and empower teams to quickly triage and manage cases involving vulnerable individuals, improving their access to necessary services.

One of the significant challenges discussed by the panel was the risk of creating a divide, where the wealthy benefit more from AI advancements while the less privileged are left behind. The panellists agreed that to prevent this, it is crucial to develop AI solutions that are inclusive and consider the diverse needs of all consumers.

Environmental impact of AI

The environmental impact of AI itself was a topic of concern. Training AI models can be energy-intensive, but advancements by companies like Google and AWS show promise in developing net zero or even net negative data centres. These centres are increasingly powered by renewable energy sources, aiming to mitigate the carbon footprint of AI technologies.

It was suggested that the carbon reducing benefits of AI in the energy transition offset the energy consumed in delivery, and while more intensive Generative AI tasks such as image creation requires a lot of power, the models used in the energy sector are much more light weight.

Guilherme challenged individuals and organisations to consider whether AI is the right tool for the problem they’ve identified, rather than driving up overall cloud energy consumption with tools that might not deliver true value. This will ensure that both financial and environmental returns are maximised.

Summary

This panel discussion shed light on the multifaceted role of AI in transforming the energy sector. By leveraging AI, we can accelerate the shift towards sustainable energy, optimise energy use, and ensure that the benefits are equitably distributed across society. As we move forward, it will be crucial to balance technological advancements with ethical considerations to create a greener and fairer future for all.

Speak to Faculty today about how AI can help your organisation embrace the energy transition.