At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award)

Patel, Shwetak; Robertson, Thomas; Kientz, Julie A.; Reynolds, Matthew S.; Abowd, Gregory D.

doi:10.1007/978-3-540-74853-3_16

Cited by 227 publications

(193 citation statements)

References 11 publications

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“…Zeifman and Roth (2011) also recently surveyed this literature; their focus is on comparing algorithmic approaches. Although electricity is the focus of the rest of this paper, the use of disaggregation for other energy-related applications is also promising (i.e., gas, water, and transportation) 22 (Yamagami, Nakamura, Meier, 1996;Cohn et al, 2010;Patel et al, 2007;Larson et al, 2010;Froehlich et al, 2009a,b).…”

Section: Disaggregation Algorithms and Their Requirementsmentioning

confidence: 99%

“…The match is typically performed by finding appropriate shifting and scaling parameters that minimize a least-square error criterion between events and exemplars. In Patel et al (2007), the authors perform household-level current sampling at 1 MHz and use transient duration and amplitude of a set of frequencies in the electric noise generated by abruptly turning on/off appliances to construct appliance signatures. They train an off-the-shelf SVM model in a preliminary calibration phase, which they use to achieve near real-time identification of ~40 appliances for 6 test homes over 6 weeks (~3000 transient events).…”

Section: Appendix B Description Of Published Algorithmsmentioning

confidence: 99%

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Is disaggregation the holy grail of energy efficiency? The case of electricity

Gupta²,

et al. 2013

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This paper aims to address two timely energy problems. First, significant low-cost energy reductions can be made in the residential and commercial sectors, but these savings have not been achievable to date. Second, billions of dollars are being spent to install smart meters, yet the energy saving and financial benefits of this infrastructure -without careful consideration of the human element -will not reach its full potential. We believe that we can address these problems by strategically marrying them, using disaggregation. Disaggregation refers to a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or wholebuilding, energy signal. In this paper, we explain how appliance level data affords numerous benefits, and why using the algorithms in conjunction with smart meters is the most cost-effective and scalable solution for getting this data. We review disaggregation algorithms and their requirements, and evaluate the extent to which smart meters can meet those requirements. Research, technology, and policy recommendations are also outlined.

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Section: Disaggregation Algorithms and Their Requirementsmentioning

confidence: 99%

Section: Appendix B Description Of Published Algorithmsmentioning

confidence: 99%

Is disaggregation the holy grail of energy efficiency? The case of electricity

Gupta²,

et al. 2013

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“…Patel et al [21] have developed a system that makes use of electrical noise signatures on residential power circuits to detect the switching of particular appliances. This demonstrates how very specific high-resolution sensing can allow micro-level events in the home to be surfaced.…”

Section: Surfacing Energy Consumption Within Spacesmentioning

confidence: 99%

“…However, considerable efforts persist in making energy measurement even more pervasive and fine-grained. For example, research is emerging within ubiquitous computing that focuses on capturing a rich picture of energy use across a broad range of settings [e.g, 10,21]. As these technologies for energy data capture mature, a number of researchers have offered the vision of a personal energy monitor that captures peoples' energy footprint throughout the day [16].…”

Section: Introductionmentioning

confidence: 99%

Exploring Reactions to Widespread Energy Monitoring

Colley

Bedwell

Crabtree

et al. 2013

Human-Computer Interaction – INTERACT 2013

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Abstract. This paper explores the measurement, apportionment and representation of widespread energy monitoring. We explicate the accountability to users of the data collected by this type of monitoring when it is presented to them as a single daylong picture. We developed a technology probe that combines energy measurement from the home, workplace and the journeys that connect these spaces. Through deployment of this probe with five users for one month we find that measurement need not be seamless for it to be accountable; that apportionment is key to making consumption for communal spaces accountable and that people can readily make useful inferences about their energy consumption from daylong pictures formed from widespread monitoring. Finally, we present four issues raised by the probe -the nature of real world monitoring, the dynamic and social nature of apportionment, disclosure of energy data and alignment of incentives with consumption -that need to be addressed in future research.

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“…Plug-level devices, such as the Kill A Watt [14] and ACme [15], cannot measure energy consumption from built-in appliances like recessed lighting. Appliance-level sensors measure indirect energy emissions (including light, sound, vibration, or electromagnetic fields) to determine appliance state but face scalability challenges [16,17]. Appliance load disaggregation methods which aim to provide appliance-level information from aggregate energy measurements are promising but their overall effectiveness is not proven, especially in large-building infrastructures [18].…”

Section: Introductionmentioning

confidence: 99%

Real-time, appliance-level electricity use feedback system: How to engage users?

Chen

Delmas

Kaiser

2014

Energy and Buildings

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a b s t r a c tEngage is a rapidly deployable, retrofit energy monitoring system developed for direct support of a novel energy use behavior investigation and a large-scale deployment in campus apartments. We describe the end-to-end system and report results related to web dashboard engagement during a year-long experiment. The objective was to determine user engagement with real-time and easily accessible information about personal energy consumption. Leveraging low-cost components, this system was designed to measure separately appliance plug load, heating and cooling, and lighting electrical load in dense-occupancy building environments. We developed and used an open source technology for measurement of plug load and developed signal processing algorithms to significantly improve measurement accuracy. We also developed proxy sensors to measure heating and cooling and lighting. Our results indicate that 90% of the dashboard activity was undertaken by 50% of the participants and that website engagement was more likely in mid-day and more effective in combination with email reminders. Energy conservation was achieved when combining the dashboard with public information about energy consumption.Published by Elsevier B.V.

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At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award)

Cited by 227 publications

References 11 publications

Is disaggregation the holy grail of energy efficiency? The case of electricity

Is disaggregation the holy grail of energy efficiency? The case of electricity

Exploring Reactions to Widespread Energy Monitoring

Real-time, appliance-level electricity use feedback system: How to engage users?

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