[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.