National Grid sees machine learning as the brains behind the utility business of the future

Another recent investment, Aperio, uses data from sensors monitoring critical infrastructure to predict loss of data quality from degradation or cyberattacks.

Indeed, of the $175 million in investments the firm has made, roughly $135 million has been committed to companies leveraging machine learning for their services.

“AI will be critical for the energy industry to achieve aggressive decarbonization and decentralization goals,” said Lisa Lambert, the chief technology and innovation officer at National Grid and the founder and president of National Grid Partners.

National Grid started the year off slowly because of the COVID-19 epidemic, but the pace of its investments picked up and the company is on track to hit its investment targets for the year, Lambert said.

Modernization is critical for an industry that still mostly runs on spreadsheets and collective knowledge that has locked in an aging employee base, with no contingency plans in the event of retirement, Lambert said. It’s that situation that’s compelling National Grid and other utilities to automate more of their business.

“Most companies in the utility sector are trying to automate now for efficiency reasons and cost reasons. Today, most companies have everything written down in manuals; as an industry, we basically still run our networks off spreadsheets, and the skills and experience of the people who run the networks. So we’ve got serious issues if those people retire. Automating [and] digitizing is top of mind for all the utilities we’ve talked to in the Next Grid Alliance.

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.”