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Can AI light the way to smarter energy use? | Ethical Corporation

Angeli Mehta looks at how companies such as Verv, DeepMind, Vigilent, Novacab and Nnergix are employing machine learning to help them rise to the climate challenge Climate change is our starkest challenge: could artificial intelligence help us meet it? There’s seemingly no aspect of the efforts we need to make that couldn’t be accelerated by artificial intelligence.

In April, a report by PwC and Microsoft suggested that across four key sectors – agriculture, energy, transport and water – AI could enable a cut in global greenhouse gas emissions of between 1.5% and 4% by 2030, with its impact greatest in transport (up to 1.7%) and in energy (up to 2.2%).

A lot of battery power is wasted keeping apps up to date in the background: predicting which apps you’re likely to use soon, compared with those you might not look at for hours or even days, saves energy.

In a recent blog post, Microsoft’s president Brad Smith said the company would launch “a new data-driven circular cloud initiative using the Internet of Things, blockchain and artificial intelligence to monitor performance and streamline our reuse, resale and recycling of data centre assets, including servers.” Microsoft’s AI platform Azure is already offering scientists new tools for monitoring the environment, and climate change mitigation.

Vigilent has committed that, together with its data centre partners, it will “use AI to eliminate wasteful cooling in data centres and telecom facilities, cutting annual carbon emissions by 50 million metric tons.” By deploying AI globally, it anticipates carbon emissions could be cut by 10 times as much.

In the longer term, he says, “I do think the technology can be delivered to other types of process industries – food processing, pharmaceuticals, indoor agriculture by using the same algorithm.” But for now Federspiel believes data centres are where the biggest impact on carbon emissions can be made.

This enhances what people do – they don’t have to deal with minute-to-minute, hour-by-hour decisions As for the fear that AI and machine learning will replace people, Federspiel thinks the opposite will be the case: “This enhances what people do – they don’t have to deal with minute-to-minute, hour-by-hour decisions on cooling.

Barcelona-based Nnergix is using AI to give grid operators, energy traders and producers around the world highly accurate energy forecasts for solar, wind and hydro production in the hours and days ahead.

“Our data analytics are helping to avoid carbon emissions because the main goal is to enable the grid integration of renewable energy sources,” explains Joan Miquel Anglès, the company’s commercial director and co-founder.

Intelligent energy storage is also going to be critical for building the electricity grids of the future Last year, Google started applying machine-learning algorithms to part of its fleet of renewable energy projects.

In transportation, fleets of delivery vehicles – which might themselves have to refrigerate goods – can maintain electrical power for heating and cooling without having to run the engine or a generator, so reducing fuel consumption and carbon dioxide emissions.

Add in smart-plugs, and AI could be used to enable the grid to manage sudden surges in energy demand by briefly switching off – say – all the fridges in an area rather than having to call on expensive back-up generation from fossil fuel plants.

Because Verv was monitoring energy consumption, it realised almost 80% of the energy produced was being sent to the grid, rather than the standard assumption of 50%, meaning that community, many of whom were paying 15p/KWh for their electricity through pre-payment meters, was being short-changed.

Refining Verv’s system could take six to 12 months, so it has full seasonal data to enable optimum decision making on when to store energy by charging up batteries, when to trade, or send energy to the grid.

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