| 1. Identificação | ||||
| Tipo de Referência | Artigo em Evento (Conference Proceedings) | |||
| Site | mtc-m16b.sid.inpe.br | |||
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Repositório | cptec.inpe.br/adm_conf/2005/10.31.20.53 | |||
| Última Atualização | 2006:04.20.12.18.06 (UTC) administrator | |||
| Repositório de Metadados | cptec.inpe.br/adm_conf/2005/10.31.20.53.04 | |||
| Última Atualização dos Metadados | 2022:03.26.18.01.22 (UTC) administrator | |||
| Chave Secundária | INPE-13855-PRE/9037 | |||
| Chave de Citação | GuarnieriPereChou:2006:SoRaFo | |||
| Título | Solar radiation forecast using artificial neural networks in south Brazil  | |||
| Formato | CD-ROM; On-line. | |||
| Ano | 2006 | |||
| Data de Acesso | 26 dez. 2024 | |||
| Tipo Secundário | PRE CI | |||
| Número de Arquivos | 1 | |||
| Tamanho | 3150 KiB | |||
| 2. Contextualização | ||||
| Autor | 1 Guarnieri, Ricardo André 2 Pereira, Enio Bueno 3 Chou, Sin Chan | |||
| Identificador de Curriculo | 1 2 8JMKD3MGP5W/3C9JH2E | |||
| Grupo | 1 RSU-INPE-MCT-BR 2 DMA-INPE-MCT-BR 3 DMD-INPE-MCT-BR | |||
| Afiliação | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Endereço de e-Mail do Autor | 1 ricardog@cptec.inpe.br 2 eniobp@cptec.inpe.br 3 chou@cptec.inpe.br | |||
| Editor | Vera, Carolina Nobre, Carlos | |||
| Endereço de e-Mail | ricardog@cptec.inpe.br | |||
| Nome do Evento | International Conference on Southern Hemisphere Meteorology and Oceanography, 8 (ICSHMO). | |||
| Localização do Evento | Foz do Iguaçu | |||
| Data | 24-28 Apr. 2006 | |||
| Editora (Publisher) | American Meteorological Society (AMS) | |||
| Cidade da Editora | 45 Beacon Hill Road, Boston, MA, USA | |||
| Páginas | 1777-1785 | |||
| Título do Livro | Proceedings | |||
| Tipo Terciário | Poster | |||
| Organização | American Meteorological Society (AMS) | |||
| Histórico (UTC) | 2005-10-31 20:53:04 :: ricardog@cptec.inpe.br -> administrator :: 2005-11-11 21:54:28 :: administrator -> adm_conf :: 2005-12-15 23:54:02 :: adm_conf -> ricardog@cptec.inpe.br :: 2006-03-28 14:00:26 :: ricardog@cptec.inpe.br -> adm_conf :: 2006-03-29 21:02:41 :: adm_conf -> ricardog@cptec.inpe.br :: 2006-03-29 23:24:56 :: ricardog@cptec.inpe.br -> administrator :: 2006-04-18 21:15:36 :: administrator -> lise@dpi.inpe.br :: 2010-12-28 12:36:45 :: lise@dpi.inpe.br -> administrator :: 2010-12-29 15:58:31 :: administrator -> lise@dpi.inpe.br :: 2006 2010-12-29 16:06:10 :: lise@dpi.inpe.br -> administrator :: 2006 2010-12-29 18:54:17 :: administrator -> banon :: 2006 2011-01-02 17:15:09 :: banon -> administrator :: 2006 2022-03-26 18:01:22 :: administrator -> :: 2006 | |||
| 3. Conteúdo e estrutura | ||||
| É a matriz ou uma cópia? | é a matriz | |||
| Estágio do Conteúdo | concluido | |||
| Transferível | 1 | |||
| Palavras-Chave | solar radiation forecast solar energy artificial neural networks Eta model SONDA project | |||
| Resumo | Incident solar radiation studies have several applications. Besides other uses we can emphasize agriculture, illumination and heating of buildings and residences, and meteorological research. Nowadays, an important application is to supply information and reliable data that support the utilization of solar radiation as a renewable energy source, with reduced impacts on the environment. Forecasting the incident solar radiation allows us to know, in advance, the resources availability. This information is important to improve the use of different energy sources in cogeneration systems. As an attempt to get a better predictability for the incident solar radiation at the surface, Artificial Neural Networks (ANNs) were used. Meteorological data provided by weather forecast models, representing the conditions of the future atmosphere, can be employed as inputs for ANNs. With a correct training, these ANNs can provide estimates of solar radiation, since the incidence of radiation at surface is totally dependent on the atmospheric conditions. In this work we have trained ANNs (Multilayer Perceptron ANNs) using as inputs some meteorological data generated by the Eta-operational Model run in the Brazilian Centro de Previsão de Tempo e Estudos Climáticos (CPTEC/INPE). Further, we have supplied the calculated solar radiation incident in the top of atmosphere, as an additional input. We have also used measurements of solar radiation incident at surface, as the targets during the training-phase. These radiation measurements were supplied by the radiometric stations of the SONDA project (Sistema de Organização Nacional de Dados Ambientais para o Setor de Energia). During the training-phase, input and target data sets were presented to the ANN. The ANN establishes mathematical relations between the input variables, with the goal of reducing the error between the calculated output and the target. The training technique used in this work was Resilient Backpropagation. After training, a new set of data was used to test the ANN performance for solar radiation forecasting. In this test-phase the solar radiation calculated by ANNs was compared with the solar radiation measurements. A comparison between radiation forecasts directly provided by the Eta model and solar radiation measurements was also accomplished, in order to verify whether there is an increase in predictability with the use of ANNs. The forecasts were tested for two SONDA-project stations: Florianópolis (lat. 27.6ºS, long. 48.5ºW) and São Matinho da Serra (lat. 29.4ºS, long. 53.8ºW). Solar radiation forecasts from ANNs presented higher correlation coefficients and lower errors than the Eta model output for shortwave radiation on ground. The well-know bias observed in solar radiation forecasts by the Eta model was removed by the use of ANNs. The improvement in RMSE obtained with ANNs over the Eta model was higher than 30%, estimated with a skill-score. | |||
| Área | MET | |||
| Tipo | Weather analysis and forecasting | |||
| Arranjo 1 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > CRCRS > Solar radiation forecast... | |||
| Arranjo 2 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDMD > Solar radiation forecast... | |||
| Arranjo 3 | urlib.net > BDMCI > Fonds > Produção até 2016 > DMA > Solar radiation forecast... | |||
| Conteúdo da Pasta doc | acessar | |||
| Conteúdo da Pasta source |
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| Conteúdo da Pasta agreement | não têm arquivos | |||
| 4. Condições de acesso e uso | ||||
| URL dos dados | http://urlib.net/ibi/cptec.inpe.br/adm_conf/2005/10.31.20.53 | |||
| URL dos dados zipados | http://urlib.net/zip/cptec.inpe.br/adm_conf/2005/10.31.20.53 | |||
| Idioma | en | |||
| Arquivo Alvo | 1777-1786.pdf | |||
| Grupo de Usuários | administrator ricardog@cptec.inpe.br administrator banon | |||
| Visibilidade | shown | |||
| Detentor da Cópia | SID/SCD | |||
| Permissão de Leitura | allow from all | |||
| 5. Fontes relacionadas | ||||
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3EUFCFP 8JMKD3MGPCW/43SKC35 8JMKD3MGPCW/46JKC45 | |||
| Lista de Itens Citando | sid.inpe.br/bibdigital/2021/01.01.17.20 10 sid.inpe.br/bibdigital/2013/10.04.21.53 6 sid.inpe.br/mtc-m21/2012/07.13.14.45.21 6 | |||
| Acervo Hospedeiro | cptec.inpe.br/nobre/2005/06.02.21.14 cptec.inpe.br/walmeida/2003/04.25.17.12 | |||
| 6. Notas | ||||
| Nota | 1 | |||
| Campos Vazios | archivingpolicy archivist callnumber contenttype copyright creatorhistory descriptionlevel dissemination documentstage doi edition identifier isbn issn label lineage mirrorrepository nextedition notes numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark url versiontype volume | |||