A synthetic energy dataset for non-intrusive load monitoring in households
Research on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its comp...
Saved in:
| Published in | Scientific data Vol. 7; no. 1; p. 108 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
02.04.2020
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Research on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM techniques provide information on present appliances and can be applied to perform diagnostics. As with related Machine Learning problems, research and development requires a sufficient amount of data to train and validate new approaches. As a viable alternative to collecting datasets in buildings during expensive and time-consuming measurement campaigns, the idea of generating synthetic datasets for NILM gain momentum recently. With SynD, we present a synthetic energy dataset with focus on residential buildings. We release 180 days of synthetic power data on aggregate level (i.e. mains) and individual appliances. SynD is the result of a custom simulation process that relies on power traces of real household appliances. In addition, we present several case studies that demonstrate similarity of our dataset and four real-world energy datasets.
Measurement(s)
energy
Technology Type(s)
computational modeling technique
Factor Type(s)
appliance •operational state
Sample Characteristic - Environment
residential building
Machine-accessible metadata file describing the reported data:
https://doi.org/10.6084/m9.figshare.11940324 |
|---|---|
| AbstractList | Abstract
Research on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM techniques provide information on present appliances and can be applied to perform diagnostics. As with related Machine Learning problems, research and development requires a sufficient amount of data to train and validate new approaches. As a viable alternative to collecting datasets in buildings during expensive and time-consuming measurement campaigns, the idea of generating synthetic datasets for NILM gain momentum recently. With SynD, we present a synthetic energy dataset with focus on residential buildings. We release 180 days of synthetic power data on aggregate level (i.e. mains) and individual appliances. SynD is the result of a custom simulation process that relies on power traces of real household appliances. In addition, we present several case studies that demonstrate similarity of our dataset and four real-world energy datasets. Research on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM techniques provide information on present appliances and can be applied to perform diagnostics. As with related Machine Learning problems, research and development requires a sufficient amount of data to train and validate new approaches. As a viable alternative to collecting datasets in buildings during expensive and time-consuming measurement campaigns, the idea of generating synthetic datasets for NILM gain momentum recently. With SynD, we present a synthetic energy dataset with focus on residential buildings. We release 180 days of synthetic power data on aggregate level (i.e. mains) and individual appliances. SynD is the result of a custom simulation process that relies on power traces of real household appliances. In addition, we present several case studies that demonstrate similarity of our dataset and four real-world energy datasets. Measurement(s) energy Technology Type(s) computational modeling technique Factor Type(s) appliance •operational state Sample Characteristic - Environment residential building Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.11940324 Research on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM techniques provide information on present appliances and can be applied to perform diagnostics. As with related Machine Learning problems, research and development requires a sufficient amount of data to train and validate new approaches. As a viable alternative to collecting datasets in buildings during expensive and time-consuming measurement campaigns, the idea of generating synthetic datasets for NILM gain momentum recently. With SynD, we present a synthetic energy dataset with focus on residential buildings. We release 180 days of synthetic power data on aggregate level (i.e. mains) and individual appliances. SynD is the result of a custom simulation process that relies on power traces of real household appliances. In addition, we present several case studies that demonstrate similarity of our dataset and four real-world energy datasets.Measurement(s)energyTechnology Type(s)computational modeling techniqueFactor Type(s)appliance •operational stateSample Characteristic - Environmentresidential buildingMachine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11940324 Research on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM techniques provide information on present appliances and can be applied to perform diagnostics. As with related Machine Learning problems, research and development requires a sufficient amount of data to train and validate new approaches. As a viable alternative to collecting datasets in buildings during expensive and time-consuming measurement campaigns, the idea of generating synthetic datasets for NILM gain momentum recently. With SynD, we present a synthetic energy dataset with focus on residential buildings. We release 180 days of synthetic power data on aggregate level (i.e. mains) and individual appliances. SynD is the result of a custom simulation process that relies on power traces of real household appliances. In addition, we present several case studies that demonstrate similarity of our dataset and four real-world energy datasets. Measurement(s) energy Technology Type(s) computational modeling technique Factor Type(s) appliance •operational state Sample Characteristic - Environment residential building Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11940324 Research on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM techniques provide information on present appliances and can be applied to perform diagnostics. As with related Machine Learning problems, research and development requires a sufficient amount of data to train and validate new approaches. As a viable alternative to collecting datasets in buildings during expensive and time-consuming measurement campaigns, the idea of generating synthetic datasets for NILM gain momentum recently. With SynD, we present a synthetic energy dataset with focus on residential buildings. We release 180 days of synthetic power data on aggregate level (i.e. mains) and individual appliances. SynD is the result of a custom simulation process that relies on power traces of real household appliances. In addition, we present several case studies that demonstrate similarity of our dataset and four real-world energy datasets.Measurement(s)energyTechnology Type(s)computational modeling techniqueFactor Type(s)appliance •operational stateSample Characteristic - Environmentresidential buildingMachine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.11940324 Research on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM techniques provide information on present appliances and can be applied to perform diagnostics. As with related Machine Learning problems, research and development requires a sufficient amount of data to train and validate new approaches. As a viable alternative to collecting datasets in buildings during expensive and time-consuming measurement campaigns, the idea of generating synthetic datasets for NILM gain momentum recently. With SynD, we present a synthetic energy dataset with focus on residential buildings. We release 180 days of synthetic power data on aggregate level (i.e. mains) and individual appliances. SynD is the result of a custom simulation process that relies on power traces of real household appliances. In addition, we present several case studies that demonstrate similarity of our dataset and four real-world energy datasets. |
| ArticleNumber | 108 |
| Author | Herold, Manuel Kovatsch, Christoph Elmenreich, Wilfried Klemenjak, Christoph |
| Author_xml | – sequence: 1 givenname: Christoph orcidid: 0000-0002-0113-6351 surname: Klemenjak fullname: Klemenjak, Christoph email: klemenjak@ieee.org organization: Institute of Networked and Embedded Systems, University of Klagenfurt – sequence: 2 givenname: Christoph surname: Kovatsch fullname: Kovatsch, Christoph organization: Institute of Networked and Embedded Systems, University of Klagenfurt – sequence: 3 givenname: Manuel surname: Herold fullname: Herold, Manuel organization: Institute of Networked and Embedded Systems, University of Klagenfurt – sequence: 4 givenname: Wilfried surname: Elmenreich fullname: Elmenreich, Wilfried organization: Institute of Networked and Embedded Systems, University of Klagenfurt |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32242026$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kUtPHDEQhC0E4rHhB3CJLHEe0m57PZ4LEkI8EiFxSc6WGffsGu3aG3sGaf89M1oC5JCcbMnlqq-7Tth-TJEYOxNwIUCab0WJeVNXgFCBkqrSe-wYYY6VUlruf7ofsdNSngFASAXzGg7ZkURUCKiP2Y8rXraxX1IfWk6R8mLLvetdoZ53KfMxtAqxz0MJL8RXyXm-TjH0KYe44CHyZRoKLdPKly_soHOrQqdv54z9ur35eX1fPTzefb--eqha1Zi-chqdME3ja5w3yjWeaqOoRlKt09o_dVg3yrTQNmCIDJD2XmKHXkqsHYKcscud72Z4WpNvacRzK7vJYe3y1iYX7N8vMSztIr3YWggjxoXM2PmbQU6_Byq9fU5DjiOzRTWSgQEp_6uSRkttjJ5gxE7V5lRKpu6dQ4CderK7nuzYk516slP-188DvP_408oowJ2gbKY1U_6I_rfrK0sQn1o |
| CitedBy_id | crossref_primary_10_1073_pnas_2205772119 crossref_primary_10_1016_j_egyai_2023_100308 crossref_primary_10_1016_j_enpol_2022_112886 crossref_primary_10_3390_en13205371 crossref_primary_10_1016_j_enbuild_2022_111951 crossref_primary_10_1109_TSG_2022_3152147 crossref_primary_10_1016_j_simpa_2023_100468 crossref_primary_10_1051_e3sconf_202123604006 crossref_primary_10_1038_s41597_020_00712_x crossref_primary_10_3390_s21093133 crossref_primary_10_3390_s22155872 crossref_primary_10_3390_en15114141 crossref_primary_10_1016_j_enbuild_2024_113890 crossref_primary_10_3233_ICA_230726 crossref_primary_10_3390_en14227609 crossref_primary_10_1016_j_mex_2023_102464 crossref_primary_10_3390_app13095755 crossref_primary_10_3390_en15051837 crossref_primary_10_3390_en17091992 crossref_primary_10_1016_j_enbuild_2021_111791 crossref_primary_10_1109_TCE_2023_3324921 crossref_primary_10_1016_j_apenergy_2022_118627 crossref_primary_10_1016_j_enbuild_2021_111523 crossref_primary_10_1049_gtd2_12330 crossref_primary_10_1038_s41597_021_01082_8 crossref_primary_10_1038_s41597_022_01252_2 crossref_primary_10_3390_s21227509 crossref_primary_10_46481_jnsps_2023_1208 crossref_primary_10_1016_j_jclepro_2024_142751 crossref_primary_10_1016_j_segan_2024_101405 crossref_primary_10_1016_j_epsr_2020_106921 crossref_primary_10_1109_TSG_2022_3189598 crossref_primary_10_3390_en14092390 crossref_primary_10_1109_TIM_2024_3406779 crossref_primary_10_3390_en16020991 crossref_primary_10_1016_j_iot_2024_101175 crossref_primary_10_3390_en16041611 crossref_primary_10_3390_en16135115 crossref_primary_10_1109_ACCESS_2024_3382830 crossref_primary_10_1016_j_segan_2023_101142 crossref_primary_10_1038_s41597_022_01914_1 crossref_primary_10_1016_j_jobe_2024_109804 crossref_primary_10_1109_TIM_2023_3261907 crossref_primary_10_3390_en17081944 |
| Cites_doi | 10.6084/m9.figshare.c.4716179 10.1109/18.61115 10.1109/TSG.2015.2494592 10.1016/j.apenergy.2016.03.038 10.1007/s12053-014-9306-2 10.3390/s121216838 10.1109/TSG.2018.2818167 10.3233/AIS-170422 10.1109/5.192069 10.1007/s10462-018-9613-7 10.3390/en12091696 10.1109/SmartGridComm.2014.7007698 10.1002/widm.1265 10.1109/SmartGridComm.2016.7778841 10.1145/3360322.3360844 10.1145/3360322.3360867 10.1145/2602044.2602051 10.1145/3137133.3141458 10.1145/2674061.2674064 10.1109/COMPSACW.2014.97 10.1109/SmartGridComm.2017.8340657 10.1109/ISGT45199.2020.9087706 10.1038/sdata.2015.7 10.1145/2821650.2821659 10.1109/EEEIC.2015.7165334 10.1109/IGCC.2013.6604512 10.1109/CCECE.2016.7726787 10.1145/2487575.2488203 10.1109/SmartGridComm.2017.8340730 10.1109/ICCP.2018.8516617 10.1109/TIT.2003.813506 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2020 The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2020 – notice: The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C NPM AAYXX CITATION 3V. 7X7 7XB 88E 8FE 8FH 8FI 8FJ 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M7P PIMPY PQEST PQQKQ PQUKI PRINS 5PM |
| DOI | 10.1038/s41597-020-0434-6 |
| DatabaseName | Springer_OA刊 PubMed CrossRef ProQuest Central (Corporate) ProQuest_Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central ProQuest Central Essentials Biological Science Collection AUTh Library subscriptions: ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection (Proquest) (PQ_SDU_P3) ProQuest Health & Medical Complete (Alumni) Biological Sciences Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Biological Science Database Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China PubMed Central (Full Participant titles) |
| DatabaseTitle | PubMed CrossRef Publicly Available Content Database ProQuest Central Student ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China ProQuest Central Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Biological Science Collection ProQuest Medical Library (Alumni) ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest Central (Alumni) |
| DatabaseTitleList | CrossRef Publicly Available Content Database Publicly Available Content Database PubMed |
| Database_xml | – sequence: 1 dbid: C6C name: Springer_OA刊 url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: BENPR name: AUTh Library subscriptions: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) |
| EISSN | 2052-4463 |
| ExternalDocumentID | 10_1038_s41597_020_0434_6 32242026 |
| Genre | Journal Article |
| GroupedDBID | 0R~ 3V. 53G 5VS 7X7 88E 8FE 8FH 8FI 8FJ AAJSJ ABUWG ACGFS ACSFO ACSMW ADBBV ADRAZ AFKRA AGHDO AJTQC ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK EBLON EBS EJD FYUFA GROUPED_DOAJ HCIFZ HMCUK HYE KQ8 LK8 M1P M48 M7P M~E NAO OK1 PGMZT PIMPY PQQKQ PROAC PSQYO RNT RNTTT RPM SNYQT UKHRP NPM AAYXX CITATION 7XB 8FK AZQEC DWQXO GNUQQ K9. PQEST PQUKI PRINS 5PM |
| ID | FETCH-LOGICAL-c498t-a62a1899d72594a9de784e72e4ca66dbf27948c0c908ee80e6dd32f2d3327a203 |
| IEDL.DBID | RPM |
| ISSN | 2052-4463 |
| IngestDate | Tue Sep 17 21:14:01 EDT 2024 Thu Oct 10 15:48:09 EDT 2024 Thu Oct 10 16:19:36 EDT 2024 Fri Aug 23 00:44:19 EDT 2024 Wed Oct 23 10:01:34 EDT 2024 Fri Oct 11 20:50:28 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c498t-a62a1899d72594a9de784e72e4ca66dbf27948c0c908ee80e6dd32f2d3327a203 |
| ORCID | 0000-0002-0113-6351 |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7118146/ |
| PMID | 32242026 |
| PQID | 2386368860 |
| PQPubID | 2041912 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_7118146 proquest_journals_2489908033 proquest_journals_2386368860 crossref_primary_10_1038_s41597_020_0434_6 pubmed_primary_32242026 springer_journals_10_1038_s41597_020_0434_6 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-04-02 |
| PublicationDateYYYYMMDD | 2020-04-02 |
| PublicationDate_xml | – month: 04 year: 2020 text: 2020-04-02 day: 02 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Scientific data |
| PublicationTitleAbbrev | Sci Data |
| PublicationTitleAlternate | Sci Data |
| PublicationYear | 2020 |
| Publisher | Nature Publishing Group UK Nature Publishing Group |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group |
| References | CR18 CR17 Nalmpantis, Vrakas (CR1) 2019; 52 CR15 Makonin, Popowich (CR6) 2015; 8 CR14 CR13 CR35 CR12 CR34 CR11 CR10 CR32 Zoha, Gluhak, Imran, Rajasegarar (CR3) 2012; 12 Hart (CR2) 1992; 80 Murray, Liao, Stankovic, Stankovic (CR30) 2016; 171 Monacchi (CR19) 2017; 9 CR4 CR5 CR8 CR7 CR28 Makonin, Popowich, Bajić, Gill, Bartram (CR31) 2015; 7 CR9 CR27 Lin (CR33) 1991; 37 CR26 CR25 CR24 CR23 CR21 CR20 Shin, Rho, Lee, Rhee (CR16) 2019; 12 Klemenjak, Kovatsch, Herold, Elmenreich (CR22) 2020 Wang, Chen, Hong, Kang (CR29) 2018; 10 434_CR11 434_CR10 434_CR32 434_CR13 434_CR35 434_CR12 434_CR34 434_CR15 434_CR14 434_CR17 J Lin (434_CR33) 1991; 37 434_CR9 D Murray (434_CR30) 2016; 171 C Shin (434_CR16) 2019; 12 Y Wang (434_CR29) 2018; 10 434_CR20 434_CR5 434_CR21 434_CR7 434_CR24 434_CR8 434_CR23 434_CR26 434_CR25 A Zoha (434_CR3) 2012; 12 434_CR28 434_CR4 434_CR27 434_CR18 A Monacchi (434_CR19) 2017; 9 S Makonin (434_CR6) 2015; 8 C Nalmpantis (434_CR1) 2019; 52 C Klemenjak (434_CR22) 2020 S Makonin (434_CR31) 2015; 7 GW Hart (434_CR2) 1992; 80 |
| References_xml | – ident: CR18 – year: 2020 ident: CR22 publication-title: figshare doi: 10.6084/m9.figshare.c.4716179 contributor: fullname: Elmenreich – ident: CR4 – ident: CR14 – ident: CR12 – ident: CR10 – volume: 37 start-page: 145 year: 1991 end-page: 151 ident: CR33 article-title: Divergence measures based on the shannon entropy publication-title: IEEE Transactions on Information theory doi: 10.1109/18.61115 contributor: fullname: Lin – ident: CR35 – ident: CR8 – ident: CR25 – ident: CR27 – volume: 7 start-page: 2575 year: 2015 end-page: 2585 ident: CR31 article-title: Exploiting hmm sparsity to perform online real-time nonintrusive load monitoring publication-title: IEEE Transactions on Smart Grid doi: 10.1109/TSG.2015.2494592 contributor: fullname: Bartram – ident: CR23 – volume: 171 start-page: 231 year: 2016 end-page: 242 ident: CR30 article-title: Understanding usage patterns of electric kettle and energy saving potential publication-title: Applied Energy doi: 10.1016/j.apenergy.2016.03.038 contributor: fullname: Stankovic – ident: CR21 – volume: 8 start-page: 809 year: 2015 end-page: 814 ident: CR6 article-title: Nonintrusive load monitoring (NILM) performance evaluation publication-title: Energy Efficiency doi: 10.1007/s12053-014-9306-2 contributor: fullname: Popowich – ident: CR15 – volume: 12 start-page: 16838 year: 2012 end-page: 16866 ident: CR3 article-title: Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey publication-title: Sensors doi: 10.3390/s121216838 contributor: fullname: Rajasegarar – ident: CR17 – volume: 10 start-page: 3125 year: 2018 end-page: 3148 ident: CR29 article-title: Review of smart meter data analytics: Applications, methodologies, and challenges publication-title: IEEE Transactions on Smart Grid doi: 10.1109/TSG.2018.2818167 contributor: fullname: Kang – volume: 9 start-page: 147 year: 2017 end-page: 162 ident: CR19 article-title: An open solution to provide personalized feedback for building energy management publication-title: Journal of Ambient Intelligence and Smart Environments doi: 10.3233/AIS-170422 contributor: fullname: Monacchi – ident: CR13 – ident: CR11 – ident: CR9 – ident: CR32 – ident: CR34 – volume: 80 start-page: 1870 year: 1992 end-page: 1891 ident: CR2 article-title: Nonintrusive appliance load monitoring publication-title: Proceedings of the IEEE doi: 10.1109/5.192069 contributor: fullname: Hart – ident: CR5 – ident: CR7 – volume: 52 start-page: 217 year: 2019 end-page: 243 ident: CR1 article-title: Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation publication-title: Artificial Intelligence Review doi: 10.1007/s10462-018-9613-7 contributor: fullname: Vrakas – volume: 12 start-page: 1696 year: 2019 ident: CR16 article-title: Data requirements for applying machine learning to energy disaggregation publication-title: Energies doi: 10.3390/en12091696 contributor: fullname: Rhee – ident: CR28 – ident: CR26 – ident: CR24 – ident: CR20 – ident: 434_CR15 – volume: 9 start-page: 147 year: 2017 ident: 434_CR19 publication-title: Journal of Ambient Intelligence and Smart Environments doi: 10.3233/AIS-170422 contributor: fullname: A Monacchi – ident: 434_CR20 doi: 10.1109/SmartGridComm.2014.7007698 – volume: 171 start-page: 231 year: 2016 ident: 434_CR30 publication-title: Applied Energy doi: 10.1016/j.apenergy.2016.03.038 contributor: fullname: D Murray – ident: 434_CR5 doi: 10.1002/widm.1265 – volume: 52 start-page: 217 year: 2019 ident: 434_CR1 publication-title: Artificial Intelligence Review doi: 10.1007/s10462-018-9613-7 contributor: fullname: C Nalmpantis – ident: 434_CR13 doi: 10.1109/SmartGridComm.2016.7778841 – volume: 37 start-page: 145 year: 1991 ident: 434_CR33 publication-title: IEEE Transactions on Information theory doi: 10.1109/18.61115 contributor: fullname: J Lin – ident: 434_CR12 doi: 10.1145/3360322.3360844 – ident: 434_CR10 doi: 10.1145/3360322.3360867 – ident: 434_CR11 doi: 10.1145/2602044.2602051 – ident: 434_CR25 – ident: 434_CR14 doi: 10.1145/3137133.3141458 – volume: 10 start-page: 3125 year: 2018 ident: 434_CR29 publication-title: IEEE Transactions on Smart Grid doi: 10.1109/TSG.2018.2818167 contributor: fullname: Y Wang – ident: 434_CR24 doi: 10.1145/2674061.2674064 – ident: 434_CR17 doi: 10.1109/COMPSACW.2014.97 – ident: 434_CR8 doi: 10.1109/SmartGridComm.2017.8340657 – ident: 434_CR32 – ident: 434_CR7 doi: 10.1109/ISGT45199.2020.9087706 – ident: 434_CR26 doi: 10.1038/sdata.2015.7 – volume: 80 start-page: 1870 year: 1992 ident: 434_CR2 publication-title: Proceedings of the IEEE doi: 10.1109/5.192069 contributor: fullname: GW Hart – ident: 434_CR23 doi: 10.1145/2821650.2821659 – ident: 434_CR4 doi: 10.1109/EEEIC.2015.7165334 – volume: 12 start-page: 1696 year: 2019 ident: 434_CR16 publication-title: Energies doi: 10.3390/en12091696 contributor: fullname: C Shin – volume: 7 start-page: 2575 year: 2015 ident: 434_CR31 publication-title: IEEE Transactions on Smart Grid doi: 10.1109/TSG.2015.2494592 contributor: fullname: S Makonin – ident: 434_CR9 doi: 10.1109/IGCC.2013.6604512 – ident: 434_CR21 doi: 10.1109/CCECE.2016.7726787 – ident: 434_CR27 doi: 10.1145/2487575.2488203 – volume: 12 start-page: 16838 year: 2012 ident: 434_CR3 publication-title: Sensors doi: 10.3390/s121216838 contributor: fullname: A Zoha – ident: 434_CR18 doi: 10.1109/SmartGridComm.2017.8340730 – volume: 8 start-page: 809 year: 2015 ident: 434_CR6 publication-title: Energy Efficiency doi: 10.1007/s12053-014-9306-2 contributor: fullname: S Makonin – ident: 434_CR28 doi: 10.1109/ICCP.2018.8516617 – ident: 434_CR34 doi: 10.1109/TIT.2003.813506 – ident: 434_CR35 – year: 2020 ident: 434_CR22 doi: 10.6084/m9.figshare.c.4716179 contributor: fullname: C Klemenjak |
| SSID | ssj0001340570 |
| Score | 2.5183752 |
| Snippet | Research on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key... Abstract Research on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a... |
| SourceID | pubmedcentral proquest crossref pubmed springer |
| SourceType | Open Access Repository Aggregation Database Index Database Publisher |
| StartPage | 108 |
| SubjectTerms | 639/166/987 639/4077/4073 Climate change Computer applications Data Descriptor Datasets Humanities and Social Sciences Learning algorithms Machine learning multidisciplinary R&D Research & development Science Science (multidisciplinary) Smart grid technology |
| SummonAdditionalLinks | – databaseName: AUTh Library subscriptions: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LSwMxEA4-Ll7Et9UqOXjwQWiapNn0JFUUERQRC70tu0kWC7qtbD34753ZzbbUqscluyQ7k5n5JvMIISddACHWeMfAlnSYspZjfNcyAKM6xfYsLsNq5IdHfddX94POIBy4FSGtstaJpaJ2I4tn5C0wLVpqYzS_HH8wvDUKo6vhCo1lsiraCsO0q1c3j0_Ps1MWiYBkGs6UplWAxcIGpAKzGqViet4gLaDMxWTJHxHT0hDdbpD1gCBpr2L5Jlny-RbZDDJa0NPQSPpsm9z3aPGVA8KDN6kvi_woZoQWfkIBq1Lw_Nkwx6oLUHn0bZQ4-l6KOE5Mhzl9HX0WHuNTxQ7p3968XN-xcHcCs6prJizRImmDL-Ui8G9U0nU-MspHwiubaO3STIAgGsttlxvvDffaOSky4aQUUSK43CUrsAq_TygHJvPUtCML4Mp0eCodPFvvjLbOu6xBzmsCxuOqRUZchraliStqx0DtGKkd6wZp1iSOg7QU8Yy3vw8r-BFAtlI2yF7FjOlEoI-UAD-yQaI5Nk1fwPbZ8yP58LVsox1hza2CLy9qhs6m_HP9B_-v_5CsiWprwQ5rkhVgoT8C1DJJj8PW_AZBH-vy priority: 102 providerName: ProQuest – databaseName: Scholars Portal Journals: Open Access dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LTwIxEJ4gXrwY8Ymi6cGDj6yWtnS7B2OIkRASPEnCbbPblkCii7qYyL93ug8Mgt520zbtzqPzzU5nCnAeIAjRyhoPbUnLE1pTF9_VHoJRGbvyLGbkspH7T7I7EL1ha1iB8nqrgoDpWtfO3Sc1-Hi5-Xqf36PC3-Up4-o2RSPkaooyd1CRC09uwCbDByfw_QLtZ79cuEMni9jmupHL1mkFcq6enPwVPs2sUmcHtgs4Sdo5_2tQscku1AqFTclFUVX6cg96bZLOE4R72JPYLOOPuOOhqZ0RBK4kmSbeJHEpGLj_kZdpZMhrpu9uYjJJyHj6mVoXrEr3YdB5fH7oesVFCp4WgZp5kWRREx0r46OzI6LAWF8J6zMrdCSliUcMtVJpqgOqrFXUSmM4GzHDOfMjRvkBVHEV9ggIRY7TWDV9jUhLtWjMDb5ra5TUxppRHa5KAoZveb2MMItzcxXm1A6R2qGjdijr0ChJHJacDxFDSC6VknR9s8APQZjLeR0Oc2YsJsLNSTB0KuvgL7Fp0cHV0l5uSSbjrKa27xJwBY68Lhn6M-Wf6z_-f_0nsMVy0UIJa0AVWWhPEcLM4rNMML8BnsPthA priority: 102 providerName: Scholars Portal – databaseName: Springer_OA刊 dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwED5BWVgQ5RkoyAMDD0W4tuM4I6qoqkowUambldiOWgkCUrrw7zknaavQMjBGtmXnHr7vco8A3CQIQoxyNkRbEoXCGOrjuyZEMCoz357F5r4a-eVVjiZiPI2mTbNoXwvTit9z9ViigfH9QplPQuQilLuwF_Ul9QI8kIP15xTukccqbrltZdvybMDJzazIX6HRyuIMD-GggYrkqeZtF3ZccQTdRhlLctt0jL47hvETKb8LhHI4k7iqmo_41M_SLQiCUoIufjgvfHkF3m3k_TO15KPSZb8xmRdkhv6_84Go8gQmw-e3wShsfpIQGpGoRZhKlvbRabIxOjIiTayLlXAxc8KkUtosZ6hxylCTUOWcok5ay1nOLOcsThnlp9DBU7hzIBS5STPVjw2iKBXRjFt8Ns4qaayzeQD3SwLqr7oXhq5i2Fzpmtoaqa09tbUMoLcksW7UotSIDySXSkm6fVjgiyCE5TyAs5oZq43w4hEMHcYA4habVhN8n-z2SDGfVf2yY19cK3Dlw5Kh6y3_PP_Fv2Zfwj6rJQ0Frgcd5Ki7QrSyyK4rOf0BSD7i9Q priority: 102 providerName: Springer Nature |
| Title | A synthetic energy dataset for non-intrusive load monitoring in households |
| URI | https://link.springer.com/article/10.1038/s41597-020-0434-6 https://www.ncbi.nlm.nih.gov/pubmed/32242026 https://www.proquest.com/docview/2386368860 https://www.proquest.com/docview/2489908033 https://pubmed.ncbi.nlm.nih.gov/PMC7118146 |
| Volume | 7 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV05T8MwFH6CsrAgyhmOygMDh0KN7druWCoQqlSEOKRuUWK7ohINSCkD_55nJyn3wpIosSU774i_53cY4KCLIMRoZ2NcSzqxMIZ6_66JEYzKzJdnsWOfjTy8llcPYjDqjBagU-fChKB9k01O86fpaT55DLGVL1PTruPE2jfDvvLZkkK2F2FRcf7JRA8bK9xjkLkHk-t2gYuUrznKfCAjF7E_tgjlWLBQUuHzcvQDY_4MlfzmLw3L0OUqrFT4kfTKeTZhweVr0Kw0tCCHVRnpo3UY9EjxliO-w57EhRQ_4uNBCzcjiFQJ2v3xJPc5F_jDI0_PqSXToOB-YDLJyePza-G8d6rYgIfLi_v-VVydnBAb0dWzOJUsPUNLyiq0bkTatU5p4RRzwqRS2mzMUA21oaZLtXOaOmktZ2NmOWcqZZRvQgNn4baBUGQxzfSZMgitdIdm3OKzcVZLY50dR3BcEzB5KQtkJMGxzXVSEj5Bwiee8ImMYK8mcVLpSpEgaJBcai3p780CPwRxLecRbJXMmA9UczEC9YVN8w6-ePbXFpSpUES7kqEITmqGfgz55_x3_j3OLiyzUgBRDveggdx1-whnZlkLhXikWrDU6w3uBng_v7i-ucW3fdlvhS0CvA6FbgUxfwc7U_od |
| link.rule.ids | 230,315,733,786,790,870,891,12083,21416,24346,27955,27956,31752,33777,41153,42222,43343,43838,51609,53825,53827,74100,74657 |
| linkProvider | National Library of Medicine |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LTxsxEB5RONBLVR5tw6P1gQNQWRjbsZ1TFSGiECAnkHKzdm1HRIIN1aYH_j0zu5tEgbbHlXdl74xn5hvPwwBHHQQhwaXI0Za0uQ5BUHw3cASjJqf2LHFM1ci3Q9O_14NRe9QcuJVNWuVcJ1aKOk4DnZGfoWkxyjhnxK_n35xujaLoanOFxgfY0Epp2ud2ZJdnLIrgyCKYqdxZifaK2o9KymlUmptVc_QOY75PlXwTL63MUO8zfGrwI-vWDN-CtVRsw1YjoSU7btpIn-zAoMvKlwLxHb7JUlXixygftEwzhkiVod_PJwXVXKDCY4_TLLKnSsBpYjYp2MP0T5koOlXuwn3v8u6iz5ubE3jQHTfjmZHZOXpS0aJ3o7NOTNbpZGXSITMm5mOJYuiCCB3hUnIimRiVHMuolLSZFOoLrOMq0jdgAlkscnduA0Ir1xa5ivgcUnQmxBTHLTidE9A_1w0yfBXYVs7X1PZIbU_U9qYFB3MS-0ZWSr_k7N-HNf4I4lqlWvC1ZsZiItRGWqIX2QK7wqbFC9Q8e3WkmDxUTbQtVdxq_PLnnKHLKf-5_r3_r_8HbPbvbm_8zdXweh8-ynqb4W47gHVkZzpE_DLLv1eb9BV3rO15 |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3NT9swFH8aTEK7TGNfdGPMhx3YJqvGdm33NCGgYmxDHEDqzUpsR60EKVO6A_897yVuq46NY-REdt7nz34fBvg0RBASXIocfcmA6xAExXcDRzBqSmrPEiuqRv51bk6v9Nl4MM75T01Oq1zYxNZQx1mgM_I-uhajjHNG9KucFnFxPPp2-5vTDVIUac3XaWzAU_SSgq5xsGO7Om9RBE2WgU3l-g36LmpFKim_UWlu1l3TA7z5MG3yr9hp65JGL-B5xpLssGP-NjxJ9UvYztrasP3cUvrzKzg7ZM1djVgP32SpLfdjlBvapDlD1MrqWc2nNdVfoPFj17MisptW2WliNq3ZZPanSRSpal7D1ejk8uiU51sUeNBDN-eFkcUB7qqixZ2OLoYxWaeTlUmHwphYVhJV0gURhsKl5EQyMSpZyaiUtIUU6g1s4irSDjCB7BalO7ABYZYbiFJFfA4pOhNiilUPviwI6G-7Zhm-DXIr5ztqe6S2J2p704PdBYl91pvGr7j872GNP4IYV6kevO2YsZwILZOWuKPsgV1j0_IFaqS9PlJPJ21DbUvVtxq__Lpg6GrK_67_3ePr_whbKJ_-5_fzH-_hmeykDIVtFzaRm-kDQpl5udfK6D13JPGl |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+synthetic+energy+dataset+for+non-intrusive+load+monitoring+in+households&rft.jtitle=Scientific+data&rft.au=Klemenjak+Christoph&rft.au=Kovatsch+Christoph&rft.au=Herold%2C+Manuel&rft.au=Elmenreich+Wilfried&rft.date=2020-04-02&rft.pub=Nature+Publishing+Group&rft.eissn=2052-4463&rft.volume=7&rft.issue=1&rft_id=info:doi/10.1038%2Fs41597-020-0434-6&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2052-4463&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2052-4463&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2052-4463&client=summon |