dc.creator | Pérez Chacón, Rubén | es |
dc.creator | Luna Romera, José María | es |
dc.creator | Troncoso Lora, Alicia | es |
dc.creator | Martínez Álvarez, Francisco | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2018-05-22T07:54:24Z | |
dc.date.available | 2018-05-22T07:54:24Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Pérez Chacón, R., Luna Romera, J.M., Troncoso Lora, A., Martínez Álvarez, F. y Riquelme Santos, J.C. (2018). Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities. Energies, 11 (3) | |
dc.identifier.issn | 1996-1073 | es |
dc.identifier.uri | https://hdl.handle.net/11441/74886 | |
dc.description.abstract | New technologies such as sensor networks have been incorporated into the management
of buildings for organizations and cities. Sensor networks have led to an exponential increase in the
volume of data available in recent years, which can be used to extract consumption patterns for the
purposes of energy and monetary savings. For this reason, new approaches and strategies are needed
to analyze information in big data environments. This paper proposes a methodology to extract
electric energy consumption patterns in big data time series, so that very valuable conclusions can
be made for managers and governments. The methodology is based on the study of four clustering
validity indices in their parallelized versions along with the application of a clustering technique.
In particular, this work uses a voting system to choose an optimal number of clusters from the results
of the indices, as well as the application of the distributed version of the k-means algorithm included
in Apache Spark’s Machine Learning Library. The results, using electricity consumption for the
years 2011–2017 for eight buildings of a public university, are presented and discussed. In addition,
the performance of the proposed methodology is evaluated using synthetic big data, which cab
represent thousands of buildings in a smart city. Finally, policies derived from the patterns discovered
are proposed to optimize energy usage across the university campus. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2014-55894-C2-R | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2017-88209-C2-R | es |
dc.description.sponsorship | Junta de Andalucía P12-TIC-1728 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Energies, 11 (3) | |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Big data | es |
dc.subject | Time series clustering | es |
dc.subject | Patterns | es |
dc.subject | Smart cities | es |
dc.title | Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2014-55894-C2-R | es |
dc.relation.projectID | TIN2017-88209-C2-R | es |
dc.relation.projectID | P12-TIC-1728 | es |
dc.relation.publisherversion | http://www.mdpi.com/1996-1073/11/3/683 | es |
dc.identifier.doi | 10.3390/en11030683 | es |
idus.format.extent | 19 | es |
dc.journaltitle | Energies | es |
dc.publication.volumen | 11 | es |
dc.publication.issue | 3 | es |