3 research outputs found
A Scoping Review of Energy Load Disaggregation
Energy load disaggregation can contribute to balancing power grids by
enhancing the effectiveness of demand-side management and promoting
electricity-saving behavior through increased consumer awareness. However, the
field currently lacks a comprehensive overview. To address this gap, this paper
con-ducts a scoping review of load disaggregation domains, data types, and
methods, by assessing 72 full-text journal articles. The findings reveal that
domestic electricity consumption is the most researched area, while others,
such as industrial load disaggregation, are rarely discussed. The majority of
research uses relatively low-frequency data, sampled between 1 and 60 seconds.
A wide variety of methods are used, and artificial neural networks are the most
common, followed by optimization strategies, Hidden Markov Models, and Graph
Signal Processing approaches
Energy Data Analytics for Smart Meter Data
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