Publication
An Electric Energy Consumer Characterization Framework Based on Data Mining Techniques
| dc.contributor.author | Figueiredo, Vera | |
| dc.contributor.author | Rodrigues, Fátima | |
| dc.contributor.author | Vale, Zita | |
| dc.contributor.author | Gouveia, Joaquim Borges | |
| dc.date.accessioned | 2017-06-29T14:35:13Z | |
| dc.date.available | 2017-06-29T14:35:13Z | |
| dc.date.issued | 2005 | |
| dc.description.abstract | This paper presents an electricity consumer characterization framework based on a knowledge discovery in databases (KDD) procedure, supported by data mining (DM) techniques, applied on the different stages of the process. The core of this framework is a data mining model based on a combination of unsupervised and supervised learning techniques. Two main modules compose this framework: the load profiling module and the classification module. The load profiling module creates a set of consumer classes using a clustering operation and the representative load profiles for each class. The classification module uses this knowledge to build a classification model able to assign different consumers to the existing classes. The quality of this framework is illustrated with a case study concerning a real database of LV consumers from the Portuguese distribution company. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1109/TPWRS.2005.846234 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/9979 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | Institute of Electrical and Electronics Engineers | pt_PT |
| dc.relation.publisherversion | http://ieeexplore.ieee.org/document/1425550/ | pt_PT |
| dc.subject | Classification | pt_PT |
| dc.subject | Clustering | pt_PT |
| dc.subject | Consumer classes | pt_PT |
| dc.subject | Data mining | pt_PT |
| dc.subject | Decision trees | pt_PT |
| dc.subject | Load profiles | pt_PT |
| dc.subject | Neural networks | pt_PT |
| dc.title | An Electric Energy Consumer Characterization Framework Based on Data Mining Techniques | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.title | IEEE Transactions on Power Systems | pt_PT |
| person.familyName | Vale | |
| person.givenName | Zita | |
| person.identifier | 632184 | |
| person.identifier.ciencia-id | 721B-B0EB-7141 | |
| person.identifier.orcid | 0000-0002-4560-9544 | |
| person.identifier.rid | A-5824-2012 | |
| person.identifier.scopus-author-id | 7004115775 | |
| rcaap.rights | closedAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | ff1df02d-0c0f-4db1-bf7d-78863a99420b | |
| relation.isAuthorOfPublication.latestForDiscovery | ff1df02d-0c0f-4db1-bf7d-78863a99420b |