Publicação
Forecasting electricity demand in households using MOGA-designed artificial neural networks
| dc.contributor.author | Bot, Karol | |
| dc.contributor.author | Ruano, Antonio | |
| dc.contributor.author | Ruano, Maria | |
| dc.date.accessioned | 2021-06-21T15:53:06Z | |
| dc.date.available | 2021-06-21T15:53:06Z | |
| dc.date.issued | 2020-07 | |
| dc.description.abstract | The prediction of electricity demand plays an essential role in the building environment. It strongly contributes to making the building more energy-efficient, having the potential to increase both thermal and visual comfort of the occupants, while reducing energy consumption, by allowing the use of model predictive control. The present article focuses on the use of computational intelligence methods for prediction of the power consumption of a case study residential building, during a horizon of 12 hours. Two exogeneous variables (ambient temperature and day code) are used in the NARX model Two different time steps were considered in the simulations, as well as constrained and unconstrained model design. The study concluded that the smaller timestep and the constrained model design obtain the best power demand prediction performance. The results obtained compare very favourably with similar approaches in the literature Copyright (C) 2020 The Authors. | pt_PT |
| dc.description.sponsorship | UIDB/50022/2020, 01/SAICT/2018 | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1016/j.ifacol.2020.12.1985 | pt_PT |
| dc.identifier.issn | 2405-8963 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/16330 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Elsevier | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Electric power | pt_PT |
| dc.subject | Prediction methods | pt_PT |
| dc.subject | Neural networks | pt_PT |
| dc.subject | Multiobjective optimization | pt_PT |
| dc.title | Forecasting electricity demand in households using MOGA-designed artificial neural networks | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 8230 | pt_PT |
| oaire.citation.issue | 2 | pt_PT |
| oaire.citation.startPage | 8225 | pt_PT |
| oaire.citation.title | IFAC-PapersOnLine | pt_PT |
| oaire.citation.volume | 53 | pt_PT |
| person.familyName | Ruano | |
| person.familyName | Ruano | |
| person.givenName | Antonio | |
| person.givenName | Maria | |
| person.identifier.ciencia-id | 3B19-9F1C-E2F1 | |
| person.identifier.ciencia-id | 9811-A0DD-D5A5 | |
| person.identifier.orcid | 0000-0001-5904-2166 | |
| person.identifier.orcid | 0000-0002-6308-8666 | |
| person.identifier.orcid | 0000-0002-0014-9257 | |
| person.identifier.rid | B-4135-2008 | |
| person.identifier.rid | A-8321-2011 | |
| person.identifier.scopus-author-id | 7004284159 | |
| person.identifier.scopus-author-id | 7004483805 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | bc300719-b790-4ee2-a678-666f2b10a7e7 | |
| relation.isAuthorOfPublication | 13813664-b68b-40aa-97a9-91481a31ebf2 | |
| relation.isAuthorOfPublication | 61fc8492-d73f-46ca-a3a3-4cd762a784e6 | |
| relation.isAuthorOfPublication.latestForDiscovery | 61fc8492-d73f-46ca-a3a3-4cd762a784e6 |
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