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How AI can help meet global energy demand

How AI can help meet global energy demand

The global energy industry is facing fundamental shifts in the way it generates, sells and distributes power. The pressure is on to cut carbon emissions and, as a result, methods must be found to manage the increasing gigawatts of unpredictable, weather-dependent renewable energy flowing on to power grids. The cost of electricity is also a concern, not just for consumers, but for governments keen to keep their voters happy.

SwissCognitiveIn short, there is a global demand for clean, cheap, reliable energy – and artificial intelligence () is increasingly being used to help meet this need. Enabling the growth of low-carbon, green electricity is an application with a potentially huge long-term impact. Enabling the growth of low-carbon, green electricity is an application with a potentially huge long-term impact Renewable forms of electricity are emerging as the successors to traditional coal and gas-fired power plants. A key problem with renewable electricit y, however, is its inconsistency. A cloudy day or a string of calm, windless afternoons will cut generation and can create power shortfalls. Conversely, too much energy can be generated; in March this year, for example, a sunny, windy Portugal produced more renewable electricity than it consumed.

At present, this means backup forms of power, which can be switched on quickly, often dirty diesel generators or coal plants, are used to smooth out the troughs and costly storage solutions are required to manage peaks of excess generation.

Using to forecast and make energy-saving decisions

Aidan O’Sullivan, head of University College London’s energy and research, says using to create “forecasts for electricity demand, generation and weather can lessen the need for these backup mechanisms”, by predicting and managing fluctuations in production.

research is also investigating decision-making with a “scale and complexity that begin to exceed that manageable by a human operator”, he says. For example, could be used to manage electricity shortfalls by briefly switching off power demand across entire communities or regions. “This might be thousands of refrigerators in people’s homes or large sites of demand, such as industrial plants,” he explains. “The speed and complexity of this task requires advanced .”

The speed and complexity of this task requires advanced .

Ceding control of your home to a remote might seem like the stuff of science fiction, but the integration of into our appliances is already underway. For example, is being used to manage energy use in a device most of us use every day – mobile phones. The latest iteration of Google’s Android phone operating system includes a function which studies your app habits to ensure battery is deployed only on the ones you like the most. Meanwhile, rarely used apps, which would previously hum away in the background consuming power, are shut down.

can now also work out how much electricity each of your home appliances is using, too. UK startup Verv uses to find the “fingerprint” of each appliance, using data from your electricity meter. Home appliance manufacturers will come under increasing pressure to produce energy-efficient products. With access to exactly what it costs to run a dishwasher or TV, consumers could rapidly become disenchanted with power-hungry devices. […]

  1. Airship Internet

    @SwissCognitive Good day to you and your team

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