3 ways artifical intelligence is transforming renewable energy

AI has been heralded as a game changing technology for many industries, and it may be the use of AI in the energy industry that is so far one of the most exciting applications.

AI brings an exciting opportunity to make energy engaging for customers, unravelling some of the mysteries behind energy and bringing intuitive ways to optimise energy supply and use.

AI and energy storage

Battery storage is one of the most freeing new technologies in the energy industry. While we’re used to batteries making our everyday lives easier – television remotes, smartphones and laptops – we’ve only just begun to see the potential of the technology applied on a large scale.

A portable storage battery plugged into a mobile phone, placed in front of a lake and mountain scene in the countryside

Batteries allow for energy to be stored and discharged. This is great because it means that excess energy can be stored at times when there is too much being produced and discharged at times of high demand.

In an era where we have more and more intermittent generation in the energy network, storage is essential to help balance any fluctuation.

However, batteries are an expensive technology. While smaller batteries are fairly inexpensive, scaling them up becomes very costly, very quickly. Over the last few years, the industry has worked on ways to make the costs fall, and supersize batteries are now becoming much more affordable for use by energy generators.

So where does AI fit in?

Thanks to data science, AI can be used to help optimise the times at which batteries can be charged and discharged, making them as cost-efficient as possible. This is something we’re already utilising here at Opus Energy – you can find out more about that here.

AI and energy forecasting

The entire energy network relies on balance. If supply and demand aren’t precisely balanced, the whole network suffers, and problems like blackouts can occur.

A white and orange striped wind sock blowing in the wind against a green countryside backdrop.

This means that network operators have a difficult job. Traditionally, in a fossil fuel-heavy network, it was easy to produce as much energy as was needed. The difficult bit was trying to forecast energy demand.

The job of forecasting is twice as difficult when a network is less reliant on fossil fuels, as there is twice as much forecasting to do: forecasting energy demand and forecasting energy generation. As renewable technologies like wind and solar power are less predictable, forecasting becomes more difficult. As anyone who lives in the UK can tell you, accurately forecasting the weather can feel near impossible… 

Luckily, forecasting is a forte of artificial intelligence. Google has used AI to forecast wind patterns and optimise energy generation via turbines at a US wind farm, which is an incredibly exciting development if it can be applied more widely.

Similarly, the roll out of smart meters will allow for much more advanced forecasting of energy demand. By analysing the large amounts of complex data, AI can find patterns that will enable sophisticated approaches to forecasting energy demand and production. This means positive knock-on effects for supply-and-demand across the entire network.

AI and energy efficiency

AI also has the potential to improve energy efficiency by reducing carbon emissions, which is important if we want to shift towards a cleaner, more sustainable future.

Two silver-coloured computer cooling fans blowing air.

In 2016, Google began using its subsidiary DeepMind (a company dedicated to AI research) to manage the energy consumption of its data centres, particularly related to cooling. The AI managed to reduce energy demand by 40%.

The UK’s National Grid has also had discussions with DeepMind to explore the potential efficiency savings that can be made, estimated to be around 10% of the UK’s total energy demand.

Introducing intelligence into this area is exciting, and it’s already underway in domestic settings. Internet-enabled devices, such as learning thermostats, can help to provide insight into energy use, anticipate demand and adjust consumption with minimum user effort.

AI is now reaching the point where it can play a serious role in the shift towards a more sustainable future. It’s an exciting time in the energy industry.

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