With artificial intelligence, it’s a brave new world for utilities
Artificial Intelligence is one of those buzzwords that conjures disparate images, depending on who you ask and where you look.
Hollywood has for years latched onto AI as a harbinger of doom and danger, along the lines of Terminator, The Matrix, and 2001: A Space Odyssey. In popular culture, IBM’s Deep Blue defeated world chess champion Garry Kasparov in 1997, and in recent years computers also bested experts in Go and Texas Hold’em.
But as the world becomes increasingly data-driven and interconnected, AI uses are quickly moving beyond abstract exhibitions and film villains. Machines can now respond to voice commands, drive cars, recommend movies and music, and make financial transactions. They can also be unleashed on social media, as the United States has recently discovered.
But assuming Bill Gates is not a bot, the founder of Microsoft dropped some interesting career advice earlier this year when he tweeted: “AI, energy, and biosciences are promising fields where you can make a huge impact. It’s what I would do if starting out today.”
As Harvard’s Franklin Wolfe writes here, “AI and energy are not mutually exclusive career paths … Although AI is in its early stages of implementation, it is poised to revolutionize the way we produce, transmit, and consume energy.”
Frequently, there is talk of how artificial intelligence could revolutionize the energy industry. From extending battery life to helping integrate renewables, and optimizing energy use to speeding outage recovery, the increasingly-connected utility industry appears well-positioned to take advantage of these computing advancements.
But as those two letters get applied to services and capabilities, sometimes more for marketing than actual ability, experts remind us that this isn’t the distant future we’re discussing. Artificial intelligence is already being used in the energy industry.
“There are many different technologies and use cases, and it is important to be specific about what the technology is we’re discussing,” said David Groarke, managing director at Indigo Advisory Group. “Utilities have been using AI for years, in terms of machine learning.”
Gorarke says there are several technologies that utilities now use to manage the grid that have some level of machine learning. On the utility side of the meter, self-healing grids are able to move power around damaged equipment to keep customer lights on. Behind the meter, in-home consumer devices are able to react to human preferences and energy price signals to maintain comfort and control cost. The Nest learning thermostat and a legion like it has been around for years, and some of those same ideas are now being used with water heaters, electric vehicle charging and HVAC systems.
“If you talk to a true computer scientist, they’ll roll their eyes,” said Gorarke of the popular culture image. “It’s a bunch of technologies — and what we’re really talking about is the machine’s ability to perform at a human level.”
Utility use cases
“Data is the new gold,” says Navigant Principal Research Analyst Neil Strother. “Information about businesses and customers is vital, and that’s where AI plays in.”
If this is a familiar refrain, then you can start to see how artificial intelligence is less about flashy future technology and more what the industry is already seeing. There are more than 70 million smart meters installed in the United States, and that total is expected to reach 90 million by 2020. The information they collect is essential to utilizing AI.
Utilities are now facing “data oceans which have overwhelmed many businesses,” said Strother. “We have all these new data streams. How do we tease out information that is meaningful?”
The goal of AI, explained Strother, is to have machines do things “smarter, better and faster.” And machine learning combined with the ability to process all of that data goes beyond simply reacting. “The machines are tuned up with all of this data and information and then they come up with solutions. … It’s a step beyond, where the digital process makes a bit of a leap.”
Take grid reliability, for instance. Self-healing grids capable of rerouting power in an outage already exist, but with artificial intelligence utility systems could be capable of predicting equipment failures and outages, helping reduce downtime and the cost of repairs.
But how can a machine predict an equipment failure? One way, says Navigant Research Analyst Michael Kelly, is to utilize a “digital twin.” Computer systems monitor the performance of a piece of equipment—a transformer, for instance, or battery–and compare that to what would be expected under those conditions. If performance and expectations do not align, it may be a sign of an impending problem.
Essentially, artificial intelligence can help move grid maintenance “from descriptive to predictive,” said Kelly.
“You’re going to start seeing more prescriptive analytics,” he said. Predicting the failure of equipment can lessen the cost of interruptions and optimize maintenance and upgrade schedules. “We’ve always had an idea of how to do predictive maintenance, but it is low-level.”
