Dataset Open Access
Godahewa, Rakshitha;
Bergmeir, Christoph;
Webb, Geoff;
Hyndman, Rob;
Montero-Manso, Pablo
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<dct:title>Electricity Weekly Dataset</dct:title> <dct:publisher> <foaf:Agent> <foaf:name>Zenodo</foaf:name> </foaf:Agent> </dct:publisher> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued> <dcat:keyword>electricity data</dcat:keyword> <dcat:keyword>forecasting</dcat:keyword> <dcat:keyword>weekly series</dcat:keyword> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2020-06-11</dct:issued> <dct:language rdf:resource="http://publications.europa.eu/resource/authority/language/ENG"/> <owl:sameAs rdf:resource="https://zenodo.org/record/4656141"/> <adms:identifier> <adms:Identifier> <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/4656141</skos:notation> <adms:schemeAgency>url</adms:schemeAgency> </adms:Identifier> </adms:identifier> <dct:relation rdf:resource="https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014"/> <dct:relation rdf:resource="http://arxiv.org/abs/1703.07015"/> <dct:relation rdf:resource="https://github.com/laiguokun/multivariate-time-series-data"/> <dct:isVersionOf rdf:resource="https://doi.org/10.5281/zenodo.3889952"/> <dct:isPartOf rdf:resource="https://zenodo.org/communities/forecasting"/> <owl:versionInfo>3</owl:versionInfo> <dct:description><p>The electricity dataset represents the electricity consumption of 370 clients recorded in 15-minutes periods in Kilowatt (kW) from 2011 to 2014.</p> <p>The uploaded dataset is an aggregated version of the hourly electricity dataset used by Lai et al. (2017). It&nbsp;contains 321 weekly time series from 2012 to 2014.</p></dct:description> <dct:description>{"references": ["UCI, 2020. Electricityloaddiagrams20112014 data set, uci machine learning repository. Accessed: 2020-04-30. URL https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}</dct:description> <dct:accessRights rdf:resource="http://publications.europa.eu/resource/authority/access-right/PUBLIC"/> <dct:accessRights> <dct:RightsStatement rdf:about="info:eu-repo/semantics/openAccess"> <rdfs:label>Open Access</rdfs:label> </dct:RightsStatement> </dct:accessRights> <dcat:distribution> <dcat:Distribution> <dct:license rdf:resource="https://creativecommons.org/licenses/by/4.0/legalcode"/> <dcat:accessURL rdf:resource="https://doi.org/10.5281/zenodo.4656141"/> </dcat:Distribution> </dcat:distribution> <dcat:distribution> <dcat:Distribution> <dcat:accessURL rdf:resource="https://doi.org/10.5281/zenodo.4656141"/> <dcat:byteSize>147816</dcat:byteSize> <dcat:downloadURL rdf:resource="https://zenodo.org/record/4656141/files/electricity_weekly_dataset.zip"/> <dcat:mediaType>application/zip</dcat:mediaType> </dcat:Distribution> </dcat:distribution> </rdf:Description> </rdf:RDF>
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