Publication
Forecasting the Portuguese electricity consumption using least-squares support vector machines
| dc.contributor.author | Ferreira, P. M. | |
| dc.contributor.author | Cuambe, D. I. | |
| dc.contributor.author | Ruano, Antonio | |
| dc.contributor.author | Pestana, Rui | |
| dc.date.accessioned | 2014-07-17T14:38:57Z | |
| dc.date.available | 2014-07-17T14:38:57Z | |
| dc.date.issued | 2013 | |
| dc.date.updated | 2014-07-16T14:48:20Z | |
| dc.description.abstract | The subject of this paper is the multi-step prediction of the Portuguese electricity consumption profile up to a 48-hour prediction horizon. In previous work on this subject, the authors have identified a radial basis function neural network one-step-ahead predictive model, which provides very good prediction accuracy and is currently in use at the Portuguese power-grid company. As the model is a static mapping employing external dynamics and the electricity consumption time-series trend and dynamics are varying with time, further work was carried out in order to test model resetting techniques as a means to update the model over time. In on-line operation, when performing the model reset procedure, it is not possible to employ the same model selection criteria as used in the MOGA identification, which results in the possibility of degraded model performance after the reset operation. This work aims to overcome that undesirable behaviour by means of least-squares support vector machines. Results are presented on the identification of such model by selecting appropriate regression window size and regressor dimension, and on the optimization of the model hyper-parameters. A strategy to update this model over time is also tested and its performance compared to that of the existing neural model. A method to initialize the hyper-parameters is proposed which avoids employing multiple random initialization trials or grid search procedures, and achieves performance above average. | por |
| dc.identifier.citation | Ferreira, P.M.; Cuambe, D. I.; Ruano, A. E.; Pestana, Rui. Forecasting the Portuguese Electricity Consumption Using Least-Squares Support Vector Machines, Trabalho apresentado em Intelligent Control and Automation Science, In 3rd IFAC International Conference on Intelligent Control and Automation Science (2013), Chengdu, 2013. | por |
| dc.identifier.doi | http://dx.doi.org/ 10.3182/20130902-3-CN-3020.00138 | |
| dc.identifier.isbn | 9783902823458 | |
| dc.identifier.other | AUT: ARU00698; | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/4782 | |
| dc.language.iso | eng | por |
| dc.peerreviewed | yes | por |
| dc.publisher | Elsevier, IFAC | por |
| dc.relation.publisherversion | http://www.ifac-papersonline.net/Detailed/63493.html | por |
| dc.subject | Real-time aspects of intelligent control | por |
| dc.subject | Training and adaptation algorithms | por |
| dc.subject | Constructive algorithms | por |
| dc.subject | Structures for computational intelligence | por |
| dc.subject | Design methodologies | por |
| dc.title | Forecasting the Portuguese electricity consumption using least-squares support vector machines | por |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.conferencePlace | Chengdu | por |
| oaire.citation.endPage | 416 | por |
| oaire.citation.issue | 1 | por |
| oaire.citation.startPage | 411 | por |
| oaire.citation.title | 3rd IFAC Intelligent Control and Automation Science Conference on Intelligente Control and Automation Science | por |
| oaire.citation.volume | 3 | por |
| person.familyName | Ruano | |
| person.givenName | Antonio | |
| person.identifier.orcid | 0000-0002-6308-8666 | |
| person.identifier.rid | B-4135-2008 | |
| person.identifier.scopus-author-id | 7004284159 | |
| rcaap.rights | restrictedAccess | por |
| rcaap.type | conferenceObject | por |
| relation.isAuthorOfPublication | 13813664-b68b-40aa-97a9-91481a31ebf2 | |
| relation.isAuthorOfPublication.latestForDiscovery | 13813664-b68b-40aa-97a9-91481a31ebf2 |