Small logo of ETH main building ETH Zurich : Computer Science : Pervasive Computing : Distributed Systems : Research : Smart Meter Services

Innovative Services based on Smart Meters

A Research Project of the Distributed Systems Group

Project period: April 2012 - February 2014

Project description

The ongoing paradigm shift in the energy sectors drives the installation of smart meters in millions of private household worldwide. Smart meters can measure electricity consumption data at a fine-grained temporal scale. Processing this data can reveal valuable context information, which forms the basis for novel services and applications. For example, household inhabitants can be provided with detailed information about the standby consumption of their household. Alternatively, providing recommendations such as when to use electricity throughout a day can help consuming energy more efficiently. These services enable integration of more and more renewable energy sources and thus pave the way towards a smart grid.

This project aims at exploring potential uses of smart meter data. Employing methods from machine learning and data mining it investigates which services can be offered to end customers – also addressing privacy constraints of such sensitive information. Throughout the project the project team performs real world deployments with two utility companies in Switzerland for data collection and evaluate our algorithms in a real world setting.

Open source code & data

The code and data resulting from the smart meter services project are publicly available. Have a look at the ECO data set, the NILM-Eval framework, and the CLASS project published on github.

Acknowledgements

This project is funded by industry partners (Energie Thun, IBAarau) and kindly supported by Landis+Gyr. We also want to thank our students (Andreas Dröscher, Christian Stücklberger, Daniel Pauli, Dominique im Obersteg, Manuel Kläy, Michael Spiegel, Romano Cicchetti, Sarah Kilcher, Steven van Damme, and Thomas Selber) for their valuable support during their lab projects, bachelor, or master theses.

See also the following related items:

Selected Publications

See the Publications of the Distributed Systems Group page for a full listing of our publications.

Related Student Projects

The following table lists corresponding student projects in our group. Note that some descriptions will be in German.

TypeTitleStudentSupervisorSemester
M NILM-Eval: Disaggregation of Real-World Electricity Consumption Data Romano CicchettiChristian BeckelHS 13
B Visualization Workbench for Energy Consultants based on Electricity Consumption Data Michael SpiegelWilhelm Kleiminger,
Christian Beckel
FS 13
B Efficient Data Storage & Retrieval of Electricity Consumption Data Steven van DammeChristian BeckelFS 13
B Open Metering for Commercial Buildings Andreas BrauchliChristian Beckel,
Wilhelm Kleiminger
FS 13
B eVisualizer – Visualisation of Household Electricity Consumption Data Christian StuecklbergerWilhelm Kleiminger,
Christian Beckel
FS 13
M Sensor-Assisted Device-Level Electricity Consumption Breakdowns in Private Households Manuel KläyChristian BeckelHS 12
M Automated Energy Consulting for Private Households by Analyzing their Baseload Electricity Consumption Dominique Im OberstegChristian BeckelFS 12
L Smart Meters in the Field - A Sensor Framework for a Real World Deployment Sara Kilcher,
Andreas Dröscher
Christian Beckel,
Wilhelm Kleiminger
FS 12
L Integrating Submeters to Individually Monitor Appliances Manuel KläyMarkus Weiss,
Christian Beckel
HS 11
ETH ZurichDistributed Systems Group
Last updated January 1 1970 01:00:00 AM MET ko