Science sees differently. Home generated power, not centralised power.
Science sees differently. Home generated power, not centralised power.
To date, a lot of the automation work that’s been done has been around basic automation of business processes. But there are new capabilities on the horizon that will push the automation of different activities up the value chain, Lambert said.
“ ML is the next level — predictive maintenance of your assets, delivering for the customer. Uniphore, for example: you’re learning from every interaction you have with your customer, incorporating that into the algorithm, and the next time you meet a customer, you’re going to do better. So that’s the next generation,” Lambert said. “Once everything is digital, you’re learning from those engagements — whether engaging an asset or a human being.”
Lambert sees another source of demand for new machine learning tech in the need for utilities to rapidly decarbonize. The move away from fossil fuels will necessitate entirely new ways of operating and managing a power grid. One where humans are less likely to be in the loop.
“In the next five years, utilities have to get automation and analytics right if they’re going to have any chance at a net-zero world — you’re going to need to run those assets differently,” said Lambert. “Windmills and solar panels are not [part of] traditional distribution networks. A lot of traditional engineers probably don’t think about the need to innovate, because they’re building out the engineering technology that was relevant when assets were built decades ago — whereas all these renewable assets have been built in the era of OT/IT.”
To date, a lot of the automation work that’s been done has been around basic automation of business processes. But there are new capabilities on the horizon that will push the automation of different activities up the value chain, Lambert said.
“ ML is the next level — predictive maintenance of your assets, delivering for the customer. Uniphore, for example: you’re learning from every interaction you have with your customer, incorporating that into the algorithm, and the next time you meet a customer, you’re going to do better. So that’s the next generation,” Lambert said. “Once everything is digital, you’re learning from those engagements — whether engaging an asset or a human being.”
Lambert sees another source of demand for new machine learning tech in the need for utilities to rapidly decarbonize. The move away from fossil fuels will necessitate entirely new ways of operating and managing a power grid. One where humans are less likely to be in the loop.
“In the next five years, utilities have to get automation and analytics right if they’re going to have any chance at a net-zero world — you’re going to need to run those assets differently,” said Lambert. “Windmills and solar panels are not [part of] traditional distribution networks. A lot of traditional engineers probably don’t think about the need to innovate, because they’re building out the engineering technology that was relevant when assets were built decades ago — whereas all these renewable assets have been built in the era of OT/IT.”
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