Energy Data Analytics for Smart Meter Data

Edited by
September 2021
346 pages
  • ISBN978-3-0365-2016-2 (Hardback)
  • ISBN978-3-0365-2017-9 (PDF)

This book is a reprint of the Special Issue Energy Data Analytics for Smart Meter Data that was published in

Chemistry & Materials Science
Environmental & Earth Sciences
Physical Sciences

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike.


This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system.


This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal.
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
smart grid; nontechnical losses; electricity theft detection; synthetic minority oversampling technique; K-means cluster; random forest; smart grids; smart energy system; smart meter; GDPR; data privacy; ethics; multi-label learning; Non-intrusive Load Monitoring; appliance recognition; fryze power theory; V-I trajectory; Convolutional Neural Network; distance similarity matrix; activation current; smart grid; electric vehicle; synthetic data; exponential distribution; Poisson distribution; Gaussian mixture models; mathematical modeling; machine learning; simulation; Non-Intrusive Load Monitoring (NILM); NILM datasets; power signature; electric load simulation; data-driven approaches; electricity theft detection; smart meters; text convolutional neural networks (TextCNN); time-series classification; data annotation; non-intrusive load monitoring; semi-automatic labeling; smart meter; appliance load signatures; ambient influences; device classification accuracy; NILM; signature; load disaggregation; transients; pulse generator; smart metering; smart power grids; power consumption data; energy data processing; user-centric applications of energy data; convolutional neural network; energy consumption; energy data analytics; energy disaggregation; machine learning; non-intrusive load monitoring; real-time; smart meter data; smart meters; transient load signature; attention mechanism; deep neural network; energy disaggregation; non-intrusive load monitoring; electrical energy; load scheduling; satisfaction; Shapley Value; smart meter; solar photovoltaics; non-intrusive load monitoring; load disaggregation; NILM; review; deep learning; deep neural networks; machine learning; n/a