A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system
Resumen:
In systems with a high penetration of wind power generation, the precision of the forecasts is a critical input for the electricity dispatch planning. In this paper, we present the methodology that has been used to implement a complete update of the wind power forecast model in Uruguay. The new model increases the precision of the forecasts both in low and high power scenarios. It allows to perform a more efficient short-term electricity dispatch, improving the resource valuation, the inter-systems energy exchanges and the prevision of the wholesale electricity market spot price. According to the simulations performed, the new model increase the precision of wind power forecasts between 7% and 32%. The model is on its production phase and their results can be accessed through pronos.adme.com.uy/svg and latorrex.adme.com.uy/vates.
| 2023 | |
|
Training Adaptation models Uncertainty Wind speed Wind power generation Predictive models Wind farms Renewable energy systems Forecasting Wind energy Neural networks Wind turbine power curve |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/42459 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522940157165568 |
|---|---|
| author | Flieller Alfonso, Guillermo Francisco |
| author2 | Solari, Alfredo Bruno Cotelo, Rafael Chaer, Ruben |
| author2_role | author author author |
| author_facet | Flieller Alfonso, Guillermo Francisco Solari, Alfredo Bruno Cotelo, Rafael Chaer, Ruben |
| author_role | author |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Flieller Alfonso Guillermo Francisco, ADME : Administración del Mercado Eléctrico, Uruguay. Solari Alfredo, ADME : Administración del Mercado Eléctrico, Uruguay. Bruno Cotelo Rafael, ADME : Administración del Mercado Eléctrico, Uruguay. Chaer Ruben, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.coverage.spatial.es.fl_str_mv | Uruguay |
| dc.creator.none.fl_str_mv | Flieller Alfonso, Guillermo Francisco Solari, Alfredo Bruno Cotelo, Rafael Chaer, Ruben |
| dc.date.accessioned.none.fl_str_mv | 2024-02-15T14:45:13Z |
| dc.date.available.none.fl_str_mv | 2024-02-15T14:45:13Z |
| dc.date.issued.none.fl_str_mv | 2023 |
| dc.description.abstract.none.fl_txt_mv | In systems with a high penetration of wind power generation, the precision of the forecasts is a critical input for the electricity dispatch planning. In this paper, we present the methodology that has been used to implement a complete update of the wind power forecast model in Uruguay. The new model increases the precision of the forecasts both in low and high power scenarios. It allows to perform a more efficient short-term electricity dispatch, improving the resource valuation, the inter-systems energy exchanges and the prevision of the wholesale electricity market spot price. According to the simulations performed, the new model increase the precision of wind power forecasts between 7% and 32%. The model is on its production phase and their results can be accessed through pronos.adme.com.uy/svg and latorrex.adme.com.uy/vates. |
| dc.format.extent.es.fl_str_mv | 5 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Flieller Alfonso, G., Solari, A., Bruno Cotelo, R. y otros. A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system [en línea]. EN: 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 16-17 nov. 2023, pp. 1-5. DOI: 10.1109/ICECET58911.2023.10389491. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/42459 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.relation.ispartof.es.fl_str_mv | 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 16-17 nov. 2023, pp. 1-5. |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Training Adaptation models Uncertainty Wind speed Wind power generation Predictive models Wind farms Renewable energy systems Forecasting Wind energy Neural networks Wind turbine power curve |
| dc.title.none.fl_str_mv | A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system |
| dc.type.es.fl_str_mv | Ponencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | In systems with a high penetration of wind power generation, the precision of the forecasts is a critical input for the electricity dispatch planning. In this paper, we present the methodology that has been used to implement a complete update of the wind power forecast model in Uruguay. The new model increases the precision of the forecasts both in low and high power scenarios. It allows to perform a more efficient short-term electricity dispatch, improving the resource valuation, the inter-systems energy exchanges and the prevision of the wholesale electricity market spot price. According to the simulations performed, the new model increase the precision of wind power forecasts between 7% and 32%. The model is on its production phase and their results can be accessed through pronos.adme.com.uy/svg and latorrex.adme.com.uy/vates. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_642c2170f4e23ae333646db33cf09956 |
| identifier_str_mv | Flieller Alfonso, G., Solari, A., Bruno Cotelo, R. y otros. A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system [en línea]. EN: 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 16-17 nov. 2023, pp. 1-5. DOI: 10.1109/ICECET58911.2023.10389491. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/42459 |
| publishDate | 2023 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | Flieller Alfonso Guillermo Francisco, ADME : Administración del Mercado Eléctrico, Uruguay.Solari Alfredo, ADME : Administración del Mercado Eléctrico, Uruguay.Bruno Cotelo Rafael, ADME : Administración del Mercado Eléctrico, Uruguay.Chaer Ruben, Universidad de la República (Uruguay). Facultad de Ingeniería.Uruguay2024-02-15T14:45:13Z2024-02-15T14:45:13Z2023Flieller Alfonso, G., Solari, A., Bruno Cotelo, R. y otros. A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system [en línea]. EN: 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 16-17 nov. 2023, pp. 1-5. DOI: 10.1109/ICECET58911.2023.10389491.https://hdl.handle.net/20.500.12008/42459In systems with a high penetration of wind power generation, the precision of the forecasts is a critical input for the electricity dispatch planning. In this paper, we present the methodology that has been used to implement a complete update of the wind power forecast model in Uruguay. The new model increases the precision of the forecasts both in low and high power scenarios. It allows to perform a more efficient short-term electricity dispatch, improving the resource valuation, the inter-systems energy exchanges and the prevision of the wholesale electricity market spot price. According to the simulations performed, the new model increase the precision of wind power forecasts between 7% and 32%. The model is on its production phase and their results can be accessed through pronos.adme.com.uy/svg and latorrex.adme.com.uy/vates.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2024-01-23T12:36:38Z No. of bitstreams: 2 license_rdf: 25790 bytes, checksum: 489f03e71d39068f329bdec8798bce58 (MD5) FSBC23.pdf: 1669981 bytes, checksum: a87ce5008bb80b8405f017b6212769fc (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2024-02-14T18:41:08Z (GMT) No. of bitstreams: 2 license_rdf: 25790 bytes, checksum: 489f03e71d39068f329bdec8798bce58 (MD5) FSBC23.pdf: 1669981 bytes, checksum: a87ce5008bb80b8405f017b6212769fc (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2024-02-15T14:45:13Z (GMT). No. of bitstreams: 2 license_rdf: 25790 bytes, checksum: 489f03e71d39068f329bdec8798bce58 (MD5) FSBC23.pdf: 1669981 bytes, checksum: a87ce5008bb80b8405f017b6212769fc (MD5) Previous issue date: 20235 p.application/pdfeneng2023 International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 16-17 nov. 2023, pp. 1-5.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)TrainingAdaptation modelsUncertaintyWind speedWind power generationPredictive modelsWind farmsRenewable energy systemsForecastingWind energyNeural networksWind turbine power curveA novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity systemPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaFlieller Alfonso, Guillermo FranciscoSolari, AlfredoBruno Cotelo, RafaelChaer, RubenLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/42459/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; 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| spellingShingle | A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system Flieller Alfonso, Guillermo Francisco Training Adaptation models Uncertainty Wind speed Wind power generation Predictive models Wind farms Renewable energy systems Forecasting Wind energy Neural networks Wind turbine power curve |
| status_str | publishedVersion |
| title | A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system |
| title_full | A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system |
| title_fullStr | A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system |
| title_full_unstemmed | A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system |
| title_short | A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system |
| title_sort | A novel strategy for wind power forecast through neural networks : Applications to the uruguayan electricity system |
| topic | Training Adaptation models Uncertainty Wind speed Wind power generation Predictive models Wind farms Renewable energy systems Forecasting Wind energy Neural networks Wind turbine power curve |
| url | https://hdl.handle.net/20.500.12008/42459 |
