Preface
A low-voltage feeder trips for the third time this July, on a street with no history of trouble. The network diagram says nothing is wrong. Nothing changed at the substation. What changed happened behind the meters: a few new EV chargers, a dozen rooftop solar installs, a heat wave. Nobody designed this feeder for the grid it’s now carrying, and nobody at the utility saw it coming, because the data that would have shown it was sitting unread in fifteen-minute meter reads.
That story repeats itself, in different shapes, wherever a smart meter is already installed: the appliance nobody metered separately but the aggregate signal reveals anyway, tomorrow’s demand a retailer needs before it happens, the theft pattern hiding in a bill that looks almost normal. A smart meter reports one number, at regular intervals. Almost everything interesting is sitting in what that number implies, and almost no single source treats the problem that way: Non-Intrusive Load Monitoring (NILM) texts stop at the appliance, forecasting texts stop at the meter, and grid-planning work starts from substation data and rarely looks downstream, meter by meter [1].
This book follows that signal all the way through. NILM disaggregation turns one aggregate reading into a per-appliance breakdown. Point and probabilistic forecasting of load and photovoltaic output tell a utility, or a household, what’s coming before it happens. And once distributed energy resources start reshaping a low-voltage feeder from the edge in, that same signal underwrites anomaly detection, customer clustering, and the hosting-capacity questions a distribution operator actually has to answer. Every chapter pairs a narrative page with a companion notebook: run it top to bottom, don’t just read it.
Anthony Faustine
Principal ML Engineer · sambaiga.github.io