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EMD-DF: a data model and file format for energy disaggregation datasets

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Lucas PereiraAuthors Info & Claims

BuildSys '17: Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments

Article No.: 52, Pages 1 - 2

https://doi.org/10.1145/3137133.3141474

Published: 08 November 2017 Publication History

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Abstract

In this paper we present the Energy Monitoring and Disaggregation Data Format (EMD-DF). EMD-DF is a data model, file format, and application programming interface that was created with the intention of providing a unique interface to create, manage and access energy disaggregation datasets.

References

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Nipun Batra, Jack Kelly, and Oliver Parson. 2014. NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring. In Proceedings of the 5th International Conference on Future Energy Systems. ACM, Cambridge, UK.

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J. Kelly and W. Knottenbelt. 2014. Metadata for Energy Disaggregation. In IEEE 38th International Computer Software and Applications Conference Workshops. 578--583.

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Pereira LVelosa NPereira M(2022)A data model and file format to represent and store high frequency energy monitoring and disaggregation datasetsScientific Reports10.1038/s41598-022-14517-y12:1Online publication date: 18-Jun-2022
https://doi.org/10.1038/s41598-022-14517-y
Pereira LCosta DRibeiro M(2022)A residential labeled dataset for smart meter data analyticsScientific Data10.1038/s41597-022-01252-29:1Online publication date: 31-Mar-2022
https://doi.org/10.1038/s41597-022-01252-2
Ahajjam MBonilla Licea DEssayeh CGhogho MKobbane A(2020)MORED: A Moroccan Buildings’ Electricity Consumption DatasetEnergies10.3390/en1324673713:24(6737)Online publication date: 21-Dec-2020
https://doi.org/10.3390/en13246737
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Published In

BuildSys '17: Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments

November 2017

292 pages

ISBN:9781450355445

DOI:10.1145/3137133

General Chair:
Kamin Whitehouse
University of Virginia
,
Program Chairs:
Prabal Dutta
University of California, Berkeley
,
Hae Young Noh
Carnegie Mellon University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 08 November 2017

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BuildSys '17: The 4th ACM International Conference on Systems for Energy-Efficient Built Environments

November 8 - 9, 2017

Delft, Netherlands

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Overall Acceptance Rate 148 of 500 submissions, 30%

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Pereira LVelosa NPereira M(2022)A data model and file format to represent and store high frequency energy monitoring and disaggregation datasetsScientific Reports10.1038/s41598-022-14517-y12:1Online publication date: 18-Jun-2022
https://doi.org/10.1038/s41598-022-14517-y
Pereira LCosta DRibeiro M(2022)A residential labeled dataset for smart meter data analyticsScientific Data10.1038/s41597-022-01252-29:1Online publication date: 31-Mar-2022
https://doi.org/10.1038/s41597-022-01252-2
Ahajjam MBonilla Licea DEssayeh CGhogho MKobbane A(2020)MORED: A Moroccan Buildings’ Electricity Consumption DatasetEnergies10.3390/en1324673713:24(6737)Online publication date: 21-Dec-2020
https://doi.org/10.3390/en13246737
Pereira MVelosa NPereira L(2019)dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Intrusive Load Monitoring DatasetsData10.3390/data40301234:3(123)Online publication date: 12-Aug-2019
https://doi.org/10.3390/data4030123
Klemenjak CReinhardt APereira LMakonin SBergés MElmenreich W(2019)Electricity Consumption Data SetsProceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3360322.3360867(159-162)Online publication date: 13-Nov-2019
https://dl.acm.org/doi/10.1145/3360322.3360867
Kriechbaumer TJorde DJacobsen H(2019)Waveform Signal Entropy and Compression Study of Whole-Building Energy DatasetsProceedings of the Tenth ACM International Conference on Future Energy Systems10.1145/3307772.3328285(58-67)Online publication date: 15-Jun-2019
https://dl.acm.org/doi/10.1145/3307772.3328285
Pereira LNunes N(2018)Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A reviewWIREs Data Mining and Knowledge Discovery10.1002/widm.12658:6Online publication date: 22-May-2018
https://doi.org/10.1002/widm.1265

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Performance of Battery-Assisted Energy Harvesting DF Relay over Nakagami-m Channels

Practical Nonlinear Energy Harvesting Model in MIMO DF Relay System with Channel Uncertainty

Optimal energy-harvesting design for AF and DF two-way relay beamforming in 6G

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