The design and evaluation of prototype eco-feedback displays for fixture-level water usage data
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Accounting for energy-reliant services within everyday life at home
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Minimizing intrusiveness in home energy measurement
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Proceedings of the fourth international conference on Future energy systems
Exploring the hidden impacts of HomeSys: energy and emissions of home sensing and automation
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
HomeFlow: inferring device usage with network traces
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Graphical displays in eco-feedback: a cognitive approach
DUXU'13 Proceedings of the Second international conference on Design, User Experience, and Usability: web, mobile, and product design - Volume Part IV
Towards automated appliance recognition using an EMF sensor in NILM platforms
Advanced Engineering Informatics
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Most energy meters installed by utilities are intended primarily to support billing functions. Meters report only the aggregate energy consumption of a home or business over intervals as long as a month. In contrast, disaggregated energy usage data identified by individual devices or appliances offers a much more descriptive dataset that has the potential to inform and empower a wide variety of energy stakeholders, from homeowners and building operators to utilities and policy makers. In this article, the authors survey existing and emerging disaggregation techniques and highlight signal features that might be used to sense disaggregated data in a viable and cost-effective manner. They provide a summary of a new approach to electrical load disaggregation that uses voltage noise, including a brief overview of their sensing hardware, classification algorithms, and evaluation in 14 homes. The article concludes with a discussion of current open research problems that must be addressed before disaggregated energy sensing can be widely deployed.