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Sensor-Assisted Device-Level Electricity Consumption Breakdowns in Private Households (M)

Status: Abgeschlossen


Information and Communication technology significantly contributes to a more efficient energy use in private households. Smart Meters, for instance, play a huge role as they measure energy consumption and report it to utility companies to intelligently plan and control the upcoming Smart Grid. Analyzing smart meter data can further provide valuable information about electricity-related activities of occupants, which in turn can be used to motivate them for being more energy-efficient. Providing electricity consumption feedback on a device-level, for example, is a promising method that is expected to achieve greater saving effects than only visualizing the overall electricity consumption [1]. Existing approaches that obtain such a breakdown by processing smart meter data suffer accuracy in real world environments. However, since more and more cheap sensors are becoming available and ready to deploy, integrating additional sensor information into disaggregation algorithms increases accuracy and hopefully provides a consumption breakdown that can then be used to motivate people for a more thrifty electricity use.


The goal of this thesis is to integrate additional sensor information such as ON/OFF state of appliances with data collected by smart meters to compute a consumption breakdown for private households. Unlike an existing approach [2] we do not require every single appliance to be equipped with a sensor. Instead we integrate an estimation of so-called ghost power (i.e. electricity caused by appliances that are not monitored) into our model. The analysis is performed based on a dataset collected in 6 Swiss households using a smart meter and individual submeters for selected appliances.


[1] Fischer, C.: Feedback on Household Electricity Consumption: a Tool for Saving Energy? Energy Efficiency 1(1), 79–104 (2008)

[2] Jung, D., Savvides, A.: Estimating Building Consumption Breakdowns using ON/OFF State Sensing and Incremental Sub-meter Deployment. In: Proceedings of the 8th Conference on Embedded Network Sensing (SenSys 2010), Zurich, Switzerland (2010)

[3] C. Beckel, W. Kleiminger, T. Staake, S. 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), Beijing, China, April 2012.

Student/Bearbeitet von: Manuel Kläy
Contact/Ansprechpartner: Christian Beckel

ETH ZurichDistributed Systems Group
Last updated March 18 2015 09:54:19 PM MET cb