Publication
Short-term forecasting photovoltaic solar power for home energy management systems
| dc.contributor.author | Bot, Karol | |
| dc.contributor.author | Ruano, Antonio | |
| dc.contributor.author | Ruano, Maria | |
| dc.date.accessioned | 2021-04-12T14:25:04Z | |
| dc.date.available | 2021-04-12T14:25:04Z | |
| dc.date.issued | 2021-01-25 | |
| dc.date.updated | 2021-03-26T14:06:06Z | |
| dc.description.abstract | Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R<sup>2</sup> of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature. | pt_PT |
| dc.description.sponsorship | Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020 grant 01/SAICT/2018. Antonio Ruano acknowledges the support of Fundação para a Ciência e Tecnologia, through IDMEC, under LAETA, grant UIDB/50022/2020. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Inventions 6 (1): 12 (2021) | pt_PT |
| dc.identifier.doi | 10.3390/inventions6010012 | pt_PT |
| dc.identifier.issn | 2411-5134 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/15367 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | MDPI | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Photovoltaic power forecasting | pt_PT |
| dc.subject | Multi-objective genetic algorithms | pt_PT |
| dc.subject | Artificial neural networks | pt_PT |
| dc.subject | Home energy management systems | pt_PT |
| dc.title | Short-term forecasting photovoltaic solar power for home energy management systems | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 1 | pt_PT |
| oaire.citation.startPage | 12 | pt_PT |
| oaire.citation.title | Inventions | pt_PT |
| oaire.citation.volume | 6 | 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 |
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