Machine Learning for Smart Meter Data
A Structured Path from Meter Signal to Business Insight
A practical book for energy analytics and ML practitioners
Machine Learning for Smart Meter Data
From raw meter signal to business decision, through the ML foundations, disaggregation, and grid-edge value that make the closing forecast worth trusting, built to be run, not just read.
Meter Signal
Data & ML
Business Value
The arc every chapter follows
The book, in eight parts
Signal Foundations
ML Foundations
Your learning path
Reading a meter signal, general event and change detection, and feature-engineering concepts every later part reuses.
- 01Reading a meter signalSoon
Chapters land after Part 8 ships.
The mathematical and statistical theory behind every model this book uses as a tool: learning theory, optimization, tree ensembles, deep architectures, and the uncertainty-quantification foundations Part 8’s own forecasts depend on.
- 01What is machine learning, preciselySoon
Chapters land after Part 8 ships.
Non-intrusive load monitoring: from the aggregate signal to appliance-level insight.
Modeling the low-voltage network, simulating DER time series on it, and identifying phase from smart-meter data alone, all with open-source power-flow simulation the rest of the book reuses.
Theoretical foundations of clustering under DER, a settled methodology validated across three real utilities, then applying it to real customers across those utilities and to real feeders at scale.
Retrieval, ranking, and case-based recommendation for LV management under DER, checked against real, OpenDSS-simulated per-customer outcomes.
Voltage-violation and power-quality anomaly detection combining smart-meter data and LV topology, beyond what a single meter can see alone.
Household-scale load, PV, and EV forecastability and uncertainty, built on Twiga. The book’s closing part, feeding its own forecasts back into Part 4’s LV network model.
20 chapters shipped so far, each with a runnable notebook. Follow progress on GitHub.