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.
- Christian Beckel, Leyna Sadamori, Silvia Santini, Thorsten Staake
Automated Customer Segmentation Based on Smart Meter Data with Temperature and Daylight Sensitivity.
Proceedings of the 6th IEEE International Conference on Smart Grid Communications (SmartGridComm 2015). Miami, FL, USA. IEEE, November 2015
Abstract, BibTeX, Paper (.pdf)
- Wilhelm Kleiminger, Christian Beckel, Silvia Santini
Household Occupancy Monitoring Using Electricity Meters.
Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015). Osaka, Japan, September 2015
Abstract, BibTeX, Paper (.pdf)
- Christian Beckel, Leyna Sadamori, Thorsten Staake, Silvia Santini
Revealing Household Characteristics from Smart Meter Data.
Energy, Vol. 78, pp. 397-410, December 2014doi: 10.1016/j.energy.2014.10.025
Abstract, BibTeX, Paper (.pdf)
- Christian Beckel, Wilhelm Kleiminger, Romano Cicchetti, Thorsten Staake, Silvia Santini
The ECO Data Set and the Performance of Non-Intrusive Load Monitoring Algorithms.
Proceedings of the 1st ACM International Conference on Embedded Systems for Energy-Efficient Buildings (BuildSys 2014). Memphis, TN, USA. ACM, pp. 80-89, November 2014
Abstract, BibTeX, Paper (.pdf)
- Wilhelm Kleiminger, Christian Beckel, Thorsten Staake, Silvia Santini
Occupancy Detection from Electricity Consumption Data.
Proceedings of the 5th ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys 2013). ACM, Rome, Italy, November 2013
Abstract, BibTeX, Paper (.pdf)
Note: This paper provides the results of a preliminary investigation. To see (or cite) the full results of our occupancy classification analysis, refer to the paper above with the title "Household Occupancy Monitoring Using Electricity Meters" published in the proceedings of the UbiComp 2015 conference.
- Christian Beckel, Leyna Sadamori, Silvia Santini
Automatic Socio-Economic Classification of Households Using Electricity Consumption Data.
Proceedings of the 4th International Conference on Future Energy Systems (ACM e-Energy '13). Berkeley, CA, USA. ACM, pp. 75-86, May 2013
Abstract, BibTeX, Paper (.pdf)
Note: This paper provides the results of a preliminary analysis. To see (or cite) the full results of our household classification analysis, refer to the paper above with the title "Revealing Household Characteristics from Smart Meter Data" published in the journal "Energy".
- Markus Weiss, Friedemann Mattern, Christian Beckel
Smart Energy Consumption Feedback – Connecting Smartphones to Smart Meters.
ERCIM News, No. 92, pp. 14-15, January 2013
Abstract, BibTeX, Paper (.pdf)
- Christian Beckel, Leyna Sadamori, Silvia Santini
Towards Automatic Classification of Private Households Using Electricity Consumption Data.
Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys '12). Toronto, Canada. ACM, pp. 169-176, November 2012
Abstract, BibTeX, Paper (.pdf)
Note: This paper provides the results of a preliminary investigation. To see (or cite) the full results of our household classification analysis, refer to the paper above with the title "Revealing Household Characteristics from Smart Meter Data" published in the journal "Energy".
- Christian Beckel, Wilhelm Kleiminger, Thorsten Staake, Silvia Santini
Improving device-level electricity consumption breakdowns in private households using ON/OFF events.
ACM SIGBED Review - Special Issue on the 3rd International Workshop on Networks of Cooperating Objects (CONET 2012), Vol. 9, No. 3, ACM, pp. 32-38, New York, NY, USA, July 2012
Abstract, BibTeX, Paper (.pdf)
- Christian Beckel, Wilhelm Kleiminger, Thorsten Staake, Silvia Santini
Improving Device-level Electricity Consumption Breakdowns in Private Households Using ON/OFF Events.
Proceedings of the 3rd International Workshop on Networks of Cooperating Objects (CONET 2012). Co-located with the CPS Week 2012. Beijing, China. pp. 40-52, April 2012
Abstract, BibTeX
Note: This paper is available as a reprint for the ACM SIGBED review (see entry above). Please use the SIGBED version for citations (BibTeX).
- Wilhelm Kleiminger, Christian Beckel, Silvia Santini
Opportunistic Sensing for Efficient Energy Usage in Private Households.
Proceedings of the Smart Energy Strategies Conference 2011. Zurich, Switzerland, September 2011
BibTeX, Paper (.pdf)
Related Student Projects
The following table lists corresponding student projects in our group. Note that some descriptions will be in German.
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