Metadata for Energy Disaggregation

Kelly, Jack; Knottenbelt, William J.

doi:10.1109/compsacw.2014.97

Cited by 37 publications

(25 citation statements)

References 9 publications

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“…The role of social media cannot be underestimated and needs to be researched further, particularly as the consumer community grows over time to include more individuals who have had life-long, constant exposure to social media. It is suggested by research that lower levels of sustained energy consumption are possible when consumers are given peer comparison feedback for their peers and efficient users 12,14,15 .…”

Section: Discussionmentioning

confidence: 99%

Mobile Persuasive Design for HEMS Adaptation

Morreale

McAllister

et al. 2015

Procedia Computer Science

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With the global adoption and implementation of smart grid technology, the coherent and steady involvement of consumers is required for the successful use of available technology. Changing consumer behavior has always been a challenge, particularly for home energy monitoring systems (HEMS). This paper introduces a novel approach of personalizing the mobile application experience for the consumer by assessing individual personality traits before giving consumers a mobile application which has the potential to retrain the consumers (users) and sustain reduced electricity usage for a household. To support the extension of electricity monitoring to the mobile app market, persuasive design models were created to target major factors which would appeal to consumers, such as saving money and environmental impact. The models were shared with a sample population to determine the probability of mobile app adoption by consumers, anticipated usage patterns, interface usage rankings, and motivations. The survey results are presented here, with analysis and identification of the challenges involved in engaging consumers and changing behaviors.

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Section: Discussionmentioning

confidence: 99%

Mobile Persuasive Design for HEMS Adaptation

Morreale

McAllister

et al. 2015

Procedia Computer Science

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“…Crucially, this version of the toolkit provides support for out-of-core processing, allowing data sets to be processed which are too large to fit into volatile memory. In addition, v0.2 adds rich metadata support via the NILM Metadata project [16], allowing arbitrarily complex metering hierarchies to be described.…”

Section: Non-intrusive Load Monitoring Toolkitmentioning

confidence: 99%

Dataport and NILMTK: A building data set designed for non-intrusive load monitoring

Parson

Fisher

Hersey

et al. 2015

2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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106

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Abstract-Non-intrusive load monitoring (NILM), or energy disaggregation, is the process of using signal processing and machine learning to separate the energy consumption of a building into individual appliances. In recent years, a number of data sets have been released in order to evaluate such approaches, which contain both building-level and appliance-level energy data. However, these data sets typically cover less than 10 households due to the financial cost of such deployments, and are not released in a format which allows the data sets to be easily used by energy disaggregation researchers. To this end, the Dataport database was created by Pecan Street Inc, which contains 1 minute circuit-level and building-level electricity data from 722 households. Furthermore, the non-intrusive load monitoring toolkit (NILMTK) was released in 2014, which provides software infrastructure to support energy disaggregation research, such as data set parsers, benchmark disaggregation algorithms and accuracy metrics. This paper describes the release of a subset of the Dataport database in NILMTK format, containing one month of electricity data from 669 households. Through the release of this Dataport data in NILMTK format, we pose a challenge to the signal processing community to produce energy disaggregation algorithms which are both accurate and scalable.

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“…reactive power, real power), the geographical coordinates of each house (to enable weather data to be retrieved), the mains wiring defining the meter hierarchy (useful if a single appliance is measured at the appliance, circuit and aggregate levels), whether a single meter measures multiple appliances and whether a specific lamp is dimmable. More detail is provided in Appendix B and our full NILM Metadata schema is described in [27].…”

Section: Data Formatmentioning

confidence: 99%

“…REDD uses 'refrigerator' whilst AMPds uses 'FGE') and different names for the measured parameters. When data is first imported into NILMTK, these diverse labels are converted to a standard vocabulary [27].…”

Section: Data Formatmentioning

confidence: 99%

Nilmtk

Batra

Kelly

Parson

et al. 2014

Proceedings of the 5th International Conference on Future Energy Systems

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383

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Non-intrusive load monitoring, or energy disaggregation, aims to separate household energy consumption data collected from a single point of measurement into appliancelevel consumption data. In recent years, the field has rapidly expanded due to increased interest as national deployments of smart meters have begun in many countries. However, empirically comparing disaggregation algorithms is currently virtually impossible. This is due to the different data sets used, the lack of reference implementations of these algorithms and the variety of accuracy metrics employed. To address this challenge, we present the Non-intrusive Load Monitoring Toolkit (NILMTK); an open source toolkit designed specifically to enable the comparison of energy disaggregation algorithms in a reproducible manner. This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets. Our toolkit includes parsers for a range of existing data sets, a collection of preprocessing algorithms, a set of statistics for describing data sets, two reference benchmark disaggregation algorithms and a suite of accuracy metrics. We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.

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Metadata for Energy Disaggregation

Cited by 37 publications

References 9 publications

Mobile Persuasive Design for HEMS Adaptation

Mobile Persuasive Design for HEMS Adaptation

Dataport and NILMTK: A building data set designed for non-intrusive load monitoring

Nilmtk

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