[kaino128]

I don't know much about electric grid engineering - are there opportunities for a ML approach to increase efficiency in ways other than the sort of better supply forecasting implied by this article.

The article does mention the losses involved in long distance transmission, but surely traditional approaches can already yield fairly well optimised planning for improving this sort of efficiency?

(Finally, this article seems quite light on details to me & doesn't mention a source Google press release or anything like that with more specifics. Maybe just better supply forecasting could yield bigger benefits that I imagine...)

[kixiQu]

(copy-pasted disclaimer: I'm involved at a junior level in very-unsexy not-Google-level smart grid research using machine learning, and my power grid engineering knowledge is painfully limited.) Predicting demand is dicey dicey stuff, but throwing sensors on the grid gives a lot of information that can imply other factors. Just pulling this out of the air for an example, but: UK power is famous for having to deal with a sharp spike in demand when a big TV event is going on and then goes to commercial, because everyone in the country in synchronized fashion gets up, goes to the kitchen, and turns on the kettle for tea. (Really, it's a big deal!) Now, that's not the best example because of the dramatic nature of the spike, but you can see how power information might on some level reflect the state just before that spike: people aren't moving around their homes, vacuuming, w/e, they're in front of the TV, right? So our demand forecaster learning from the data might not be able to tell that a new season of Sherlock is airing, but it might learn enough paranoia about everyone-watching-TV-at-once patterns to be useful.

[DataDisciple]

DM is far from the first company to provide ML solutions to revolutionize utilities.

There are so many opportunities to increase the efficiency of our electric networks. Forecasting demand is not something they do well, but more importantly they could improve Demand Response and Energy Efficiency programs. Oh, and most of the techniques they use to prevent and stop theft are a joke (that's a $6B/yr problem in the US).

The utilities have not been forced to innovate. They won't innovate on their own because there are no customers at risk - no competition. (Aside from smart meters and the main benefit from that was that they no longer had to pay for meter readers.)

There is a WORLD of opportunity for utilities to become more efficient, but they will not do it on their own. Our regulators need to force them to innovate.

Kudos to DM for this work, but I will be more impressed if they can actually get a major utility to implement these solutions.

[_800MHz_]

"Forecasting demand is not something they do well, but more importantly they could improve Demand Response and Energy Efficiency programs."

- Source?

"The utilities have not been forced to innovate. They won't innovate on their own because there are no customers at risk - no competition."

- Also, what data can you provide to back this up? I work in the industry, and I can tell you that innovation will depend largely on the type of energy market that the utility operates in, whether or not they are a vertically integrated company, regulated or unregulated, IOU or POU, as well as a ton of other variables. So while maybe its true that not every single company is innovating, to generally say that they "won't innovate" or that there is "no competition" is simply wrong as well as spreading incorrect information about the industry.

I'd check out this is you're interested in learning more about the utility industry: https://www.osti.gov/scitech/biblio/15001013