| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21b.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34P/3LG2PPH | |||
| Repository | sid.inpe.br/mtc-m21b/2016/04.11.16.28 (restricted access) | |||
| Last Update | 2016:04.11.16.29.50 (UTC) administrator | |||
| Metadata Repository | sid.inpe.br/mtc-m21b/2016/04.11.16.28.48 | |||
| Metadata Last Update | 2018:06.04.02.40.41 (UTC) administrator | |||
| DOI | 10.1016/j.renene.2015.11.005 | |||
| ISSN | 0960-1481 | |||
| Citation Key | LimaMarPerLorHei:2016:FoSuSo | |||
| Title | Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks  | |||
| Year | 2016 | |||
| Month | Mar. | |||
| Access Date | 2025, Feb. 05 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 3812 KiB | |||
| 2. Context | ||||
| Author | 1 Lima, Francisco José Lopes de 2 Martins, Fernando Ramos 3 Pereira, Enio Bueno 4 Lorenz, Elke 5 Heinemann, Detlev | |||
| Resume Identifier | 1 2 3 8JMKD3MGP5W/3C9JH2E | |||
| Group | 1 CST-CST-INPE-MCTI-GOV-BR 2 3 CST-CST-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Universidade Federal de São Paulo (UNIFESP) 3 Instituto Nacional de Pesquisas Espaciais (INPE) 4 University of Oldenburg 5 Universidade Federal de São Paulo (UNIFESP) | |||
| Author e-Mail Address | 1 francisco.lopes@inpe.br 2 3 enio.pereira@inpe.br | |||
| Journal | Renewable Energy | |||
| Volume | 87 | |||
| Pages | 807-818 | |||
| Secondary Mark | A1_INTERDISCIPLINAR A1_GEOCIÊNCIAS A1_ENGENHARIAS_IV A1_ENGENHARIAS_III A1_ENGENHARIAS_II A1_ENGENHARIAS_I A1_CIÊNCIAS_AMBIENTAIS A1_CIÊNCIAS_AGRÁRIAS_I A1_ADMINISTRAÇÃO,_CIÊNCIAS_CONTÁBEIS_E_TURISMO A2_QUÍMICA A2_MATERIAIS A2_CIÊNCIA_DE_ALIMENTOS A2_BIODIVERSIDADE A2_ARQUITETURA_E_URBANISMO B1_CIÊNCIAS_BIOLÓGICAS_I B1_BIOTECNOLOGIA B1_ASTRONOMIA_/_FÍSICA | |||
| History (UTC) | 2016-04-11 16:28:48 :: simone -> administrator :: 2018-06-04 02:40:41 :: administrator -> simone :: 2016 | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Content Type | External Contribution | |||
| Version Type | publisher | |||
| Keywords | Artificial neural network Solar energy forecast Solar irradiance WRF model | |||
| Abstract | There has been a growing demand on energy sector for short-term predictions of energy resources to support the planning and management of electricity generation and distribution systems. The purpose of this work is establishing a methodology to produce solar irradiation forecasts for the Brazilian Northeastern region by using Weather Research and Forecasting Model (WRF) combined with a statistical post-processing method. The 24 h solar irradiance forecasts were obtained using the WRF model. In order to reduce uncertainties, a cluster analysis technique was employed to select areas presenting similar climate features. Comparison analysis between WRF model outputs and observational data were performed to evaluate the model skill in forecasting surface solar irradiance. Next, model-derived short-term solar irradiance forecasts from the WRF outputs were refined by using an artificial neural networks (ANNs) technique. The output variables of the WRF model representing the forecasted atmospheric conditions were used as predictors by ANNs, adjusted to calculate the solar radiation incident for the entire Brazilian Northeastern (NEB) (which was divided into four homogeneous regions, defined by the Ward method). The data used in this study was from rainy and dry seasons between 2009 and 2011. Several predictors were tested to adjust and simulate the ANNs. We found the best ANN architecture and a group of 10 predictors, in which a deeper analyzes were carried out, including performance evaluation for Fall and Spring of 2011 (rainy and dry season in NEB, mainly in the northern section). There was a significant improvement of the WRF model forecasts when adjusted by the ANNs, yielding lower bias and RMSE, and an increase in the correlation coefficient. | |||
| Area | CST | |||
| Arrangement | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > COCST > Forecast for surface... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| Language | en | |||
| Target File | Lima_forecast.pdf | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Archiving Policy | denypublisher denyfinaldraft24 | |||
| Read Permission | deny from all and allow from 150.163 | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Mirror Repository | urlib.net/www/2011/03.29.20.55 | |||
| Next Higher Units | 8JMKD3MGPCW/3F3T29H | |||
| Citing Item List | sid.inpe.br/bibdigital/2013/10.19.20.40 9 sid.inpe.br/mtc-m21/2012/07.13.14.45.21 5 | |||
| Dissemination | WEBSCI; PORTALCAPES; COMPENDEX; SCOPUS. | |||
| Host Collection | sid.inpe.br/mtc-m21b/2013/09.26.14.25.20 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||