A computational intelligence approach for solar photovoltaic power generation forecasting
This article describes an approach applying computational intelligence methods for the problem of forecasting solar photovoltaic power generation at country level. Precise forecast of power generation plays a vital role in designing a dependable photovoltaic power generation system. The computed pre...
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| Format: | article |
| Idioma: | anglès |
| Publicat: |
2024
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| Accés en línia: | https://journals.sagepub.com/doi/10.1177/27533735241237990 https://hdl.handle.net/20.500.12008/52917 |
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| _version_ | 1868890096915185664 |
|---|---|
| author | Nesmachnow, Sergio |
| author2 | Risso, Claudio |
| author2_role | author |
| author_browse | Nesmachnow, Sergio Risso, Claudio |
| author_facet | Nesmachnow, Sergio Risso, Claudio |
| author_role | author |
| collection | COLIBRI |
| dc.contributor.none.fl_str_mv | Nesmachnow Sergio, Universidad de la República (Uruguay). Facultad de Ingeniería. Risso Claudio, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.creator.none.fl_str_mv | Nesmachnow, Sergio Risso, Claudio |
| dc.date.none.fl_str_mv | 2024 2025-12-09T12:40:26Z 2025-12-09T12:40:26Z |
| dc.format.none.fl_str_mv | 18 p. application/pdf |
| dc.identifier.none.fl_str_mv | Nesmachnow, S. y Risso, C. "A computational intelligence approach for solar photovoltaic power generation forecasting". Renewable Energies. [en línea]. 2024, vol. 2, no. 1, pp. 1-18. DOI: 10.1177/27533735241237990. 2753-3735 https://journals.sagepub.com/doi/10.1177/27533735241237990 https://hdl.handle.net/20.500.12008/52917 10.1177/27533735241237990 |
| dc.language.none.fl_str_mv | en eng |
| dc.publisher.none.fl_str_mv | Sage |
| dc.relation.none.fl_str_mv | Renewable Energies, vol. 2, no. 1, jan. 2024, pp. 1-18, DOI: 10.1177/27533735241237990. |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess Licencia Creative Commons Atribución - No Comercial (CC - By-NC 4.0) |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.none.fl_str_mv | Solar photovoltaic Forecasting Neural networks Computational intelligence |
| dc.title.none.fl_str_mv | A computational intelligence approach for solar photovoltaic power generation forecasting |
| dc.type.none.fl_str_mv | Artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | This article describes an approach applying computational intelligence methods for the problem of forecasting solar photovoltaic power generation at country level. Precise forecast of power generation plays a vital role in designing a dependable photovoltaic power generation system. The computed predictions enable the implementation of efficient planning, management, and distribution strategies for the generated power, ultimately enhancing the performance and efficiency of the system. The study analyzes and compares artificial neural network approaches for a specific case study using real solar photovoltaic power generation data from Uruguay in the period 2018 to 2022. Several artificial neural network architectures are evaluated for forecasting. The main results indicate that the approach applying a combination of Encoder-Decoder and Long Short Term Memory artificial neural networks is the most effective method for the addressed forecasting problem. The approach yielded promising results, with an average mean error value of 0.09, improving over the other artificial neural network architectures. Even better results were obtained for sunny days. The generated forecasts hold significant value for its application in planning and scheduling processes, aiming to enhance the overall quality of service of the electricity grid. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | anni_7494b4fca2da648eb6f57e4fc60a9fb2 |
| identifier_str_mv | Nesmachnow, S. y Risso, C. "A computational intelligence approach for solar photovoltaic power generation forecasting". Renewable Energies. [en línea]. 2024, vol. 2, no. 1, pp. 1-18. DOI: 10.1177/27533735241237990. 2753-3735 10.1177/27533735241237990 |
| 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 | anni |
| network_name_str | oai-lr-anni |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/52917 |
| publishDate | 2024 |
| publishDateSort | 2024 |
| publisher.none.fl_str_mv | Sage |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial (CC - By-NC 4.0) |
| spelling | A computational intelligence approach for solar photovoltaic power generation forecastingNesmachnow, SergioRisso, ClaudioSolar photovoltaicForecastingNeural networksComputational intelligenceThis article describes an approach applying computational intelligence methods for the problem of forecasting solar photovoltaic power generation at country level. Precise forecast of power generation plays a vital role in designing a dependable photovoltaic power generation system. The computed predictions enable the implementation of efficient planning, management, and distribution strategies for the generated power, ultimately enhancing the performance and efficiency of the system. The study analyzes and compares artificial neural network approaches for a specific case study using real solar photovoltaic power generation data from Uruguay in the period 2018 to 2022. Several artificial neural network architectures are evaluated for forecasting. The main results indicate that the approach applying a combination of Encoder-Decoder and Long Short Term Memory artificial neural networks is the most effective method for the addressed forecasting problem. The approach yielded promising results, with an average mean error value of 0.09, improving over the other artificial neural network architectures. Even better results were obtained for sunny days. The generated forecasts hold significant value for its application in planning and scheduling processes, aiming to enhance the overall quality of service of the electricity grid.La investigación presentada en este artículo se desarrolló como parte de un proyecto conjunto entre la UTE y la Universidad de la República, Uruguay.SageNesmachnow Sergio, Universidad de la República (Uruguay). Facultad de Ingeniería.Risso Claudio, Universidad de la República (Uruguay). Facultad de Ingeniería.2025-12-09T12:40:26Z2025-12-09T12:40:26Z2024Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion18 p.application/pdfNesmachnow, S. y Risso, C. "A computational intelligence approach for solar photovoltaic power generation forecasting". Renewable Energies. [en línea]. 2024, vol. 2, no. 1, pp. 1-18. DOI: 10.1177/27533735241237990.2753-3735https://journals.sagepub.com/doi/10.1177/27533735241237990https://hdl.handle.net/20.500.12008/5291710.1177/27533735241237990reponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaenengRenewable Energies, vol. 2, no. 1, jan. 2024, pp. 1-18, DOI: 10.1177/27533735241237990.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 (CC - By-NC 4.0)oai:colibri.udelar.edu.uy:20.500.12008/529172026-04-14T10:40:17Z |
| spellingShingle | A computational intelligence approach for solar photovoltaic power generation forecasting Nesmachnow, Sergio Solar photovoltaic Forecasting Neural networks Computational intelligence |
| status_str | publishedVersion |
| title | A computational intelligence approach for solar photovoltaic power generation forecasting |
| title_full | A computational intelligence approach for solar photovoltaic power generation forecasting |
| title_fullStr | A computational intelligence approach for solar photovoltaic power generation forecasting |
| title_full_unstemmed | A computational intelligence approach for solar photovoltaic power generation forecasting |
| title_short | A computational intelligence approach for solar photovoltaic power generation forecasting |
| title_sort | A computational intelligence approach for solar photovoltaic power generation forecasting |
| topic | Solar photovoltaic Forecasting Neural networks Computational intelligence |
| url | https://journals.sagepub.com/doi/10.1177/27533735241237990 https://hdl.handle.net/20.500.12008/52917 |