Imperial College London
Alternate

ProfessorWilliamKnottenbelt

Faculty of Engineering, Department of Computing

Professor of Applied Quantitative Analysis
 
 
 
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Contact

 

+44 (0)20 7594 8331w.knottenbelt Website

 
 
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Location

 

E363ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Parson:2016:10.1109/GlobalSIP.2015.7418187,
author = {Parson, O and Fisher, G and Hersey, A and Batra, N and Kelly, J and Singh, A and Knottenbelt, W and Rogers, A},
doi = {10.1109/GlobalSIP.2015.7418187},
pages = {210--214},
publisher = {IEEE},
title = {Dataport and NILMTK: A building data set designed for non-intrusive load monitoring},
url = {http://dx.doi.org/10.1109/GlobalSIP.2015.7418187},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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.
AU  - Parson,O
AU  - Fisher,G
AU  - Hersey,A
AU  - Batra,N
AU  - Kelly,J
AU  - Singh,A
AU  - Knottenbelt,W
AU  - Rogers,A
DO  - 10.1109/GlobalSIP.2015.7418187
EP  - 214
PB  - IEEE
PY  - 2016///
SP  - 210
TI  - Dataport and NILMTK: A building data set designed for non-intrusive load monitoring
UR  - http://dx.doi.org/10.1109/GlobalSIP.2015.7418187
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000380477600044&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/106254
ER  -
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