Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast
The forecast of wind energy is closely linked to the prediction of the variation of winds over very short time intervals. Four wind towers located in the Inner Mongolia were selected to understand wind power resources in the compound plateau region. The mesoscale weather research and forecasting com...
- Autores:
-
Deng, Hua
Li, Yan
Zhang, Yingchao
Zhou, Hou
Cheng, Peipei
Wang, Jia
Ashraf, Muhammad Aqeel
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2017
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/63591
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/63591
http://bdigital.unal.edu.co/64037/
- Palabra clave:
- 55 Ciencias de la tierra / Earth sciences and geology
Wind forecast
WRF/YSU/ Noah
BP-ANN
LS-SVM
Predicción del viento
esquema de la Universidad Yonsei combinado con el Modelo de Superficie Terrestre Noah (WRF/YSU/Noah)
propagación hacia atrás en redes neuronales artificiales
máquina de vectores
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast |
| title |
Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast |
| spellingShingle |
Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast 55 Ciencias de la tierra / Earth sciences and geology Wind forecast WRF/YSU/ Noah BP-ANN LS-SVM Predicción del viento esquema de la Universidad Yonsei combinado con el Modelo de Superficie Terrestre Noah (WRF/YSU/Noah) propagación hacia atrás en redes neuronales artificiales máquina de vectores |
| title_short |
Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast |
| title_full |
Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast |
| title_fullStr |
Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast |
| title_full_unstemmed |
Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast |
| title_sort |
Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast |
| dc.creator.fl_str_mv |
Deng, Hua Li, Yan Zhang, Yingchao Zhou, Hou Cheng, Peipei Wang, Jia Ashraf, Muhammad Aqeel |
| dc.contributor.author.spa.fl_str_mv |
Deng, Hua Li, Yan Zhang, Yingchao Zhou, Hou Cheng, Peipei Wang, Jia Ashraf, Muhammad Aqeel |
| dc.subject.ddc.spa.fl_str_mv |
55 Ciencias de la tierra / Earth sciences and geology |
| topic |
55 Ciencias de la tierra / Earth sciences and geology Wind forecast WRF/YSU/ Noah BP-ANN LS-SVM Predicción del viento esquema de la Universidad Yonsei combinado con el Modelo de Superficie Terrestre Noah (WRF/YSU/Noah) propagación hacia atrás en redes neuronales artificiales máquina de vectores |
| dc.subject.proposal.spa.fl_str_mv |
Wind forecast WRF/YSU/ Noah BP-ANN LS-SVM Predicción del viento esquema de la Universidad Yonsei combinado con el Modelo de Superficie Terrestre Noah (WRF/YSU/Noah) propagación hacia atrás en redes neuronales artificiales máquina de vectores |
| description |
The forecast of wind energy is closely linked to the prediction of the variation of winds over very short time intervals. Four wind towers located in the Inner Mongolia were selected to understand wind power resources in the compound plateau region. The mesoscale weather research and forecasting combining Yonsei University scheme and Noah land surface model (WRF/YSU/Noah) with 1-km horizontal resolution and 10-min time resolution were used to be as the wind numerical weather prediction (NWP) model. Three statistical techniques, persistence, back-propagation artificial neural network (BP-ANN), and least square support vector machine (LS-SVM) were used to improve the wind speed forecasts at a typical wind turbine hub height (70 m) along with the WRF/YSU/Noah output. The current physical-statistical forecasting techniques exhibit good skill in three different time scales: (1) short-term (day-ahead); (2) immediate-short-term (6-h ahead); and (3) nowcasting (1-h ahead). The forecast method, which combined WRF/YSU/Noah outputs, persistence, and LS-SVM methods, increases the forecast skill by 26.3-49.4% compared to the direct outputs of numerical WRF/YSU/Noah model. Also, this approach captures well the diurnal cycle and seasonal variability of wind speeds, as well as wind direction. |
| publishDate |
2017 |
| dc.date.issued.spa.fl_str_mv |
2017-01-01 |
| dc.date.accessioned.spa.fl_str_mv |
2019-07-02T21:55:56Z |
| dc.date.available.spa.fl_str_mv |
2019-07-02T21:55:56Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/resource_type/c_6501 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Text |
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http://purl.org/redcol/resource_type/ART |
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http://purl.org/coar/resource_type/c_6501 |
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publishedVersion |
| dc.identifier.issn.spa.fl_str_mv |
ISSN: 2339-3459 |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/63591 |
| dc.identifier.eprints.spa.fl_str_mv |
http://bdigital.unal.edu.co/64037/ |
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ISSN: 2339-3459 |
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https://repositorio.unal.edu.co/handle/unal/63591 http://bdigital.unal.edu.co/64037/ |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.spa.fl_str_mv |
https://revistas.unal.edu.co/index.php/esrj/article/view/63004 |
| dc.relation.ispartof.spa.fl_str_mv |
Universidad Nacional de Colombia Revistas electrónicas UN Earth Sciences Research Journal Earth Sciences Research Journal |
| dc.relation.references.spa.fl_str_mv |
Deng, Hua and Li, Yan and Zhang, Yingchao and Zhou, Hou and Cheng, Peipei and Wang, Jia and Ashraf, Muhammad Aqeel (2017) Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast. Earth Sciences Research Journal, 21 (1). pp. 37-43. ISSN 2339-3459 |
| dc.rights.spa.fl_str_mv |
Derechos reservados - Universidad Nacional de Colombia |
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http://purl.org/coar/access_right/c_abf2 |
| dc.rights.license.spa.fl_str_mv |
Atribución-NoComercial 4.0 Internacional |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial 4.0 Internacional Derechos reservados - Universidad Nacional de Colombia http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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application/pdf |
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Universidad Nacional de Colombia - Sede Bogotá - Facultad de Ciencias - Departamento de Geociencia |
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Universidad Nacional de Colombia |
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Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Deng, Hua0e823340-dc55-479a-9562-fac6d47ac940300Li, Yan32fd4c5f-0abc-4f97-a308-adbff040c534300Zhang, Yingchaoa4d002fa-05a8-4868-b08e-8e1fd727964c300Zhou, Hou9e9ac001-9474-4a7a-b64d-29419adebcd9300Cheng, Peipei9481bdc6-8b3e-4e45-9fb5-d8e21965a360300Wang, Jia41d024b1-5d38-4a0d-ba1a-f8e8191da944300Ashraf, Muhammad Aqeelfa68621f-0156-462a-b33a-e05059b9cea63002019-07-02T21:55:56Z2019-07-02T21:55:56Z2017-01-01ISSN: 2339-3459https://repositorio.unal.edu.co/handle/unal/63591http://bdigital.unal.edu.co/64037/The forecast of wind energy is closely linked to the prediction of the variation of winds over very short time intervals. Four wind towers located in the Inner Mongolia were selected to understand wind power resources in the compound plateau region. The mesoscale weather research and forecasting combining Yonsei University scheme and Noah land surface model (WRF/YSU/Noah) with 1-km horizontal resolution and 10-min time resolution were used to be as the wind numerical weather prediction (NWP) model. Three statistical techniques, persistence, back-propagation artificial neural network (BP-ANN), and least square support vector machine (LS-SVM) were used to improve the wind speed forecasts at a typical wind turbine hub height (70 m) along with the WRF/YSU/Noah output. The current physical-statistical forecasting techniques exhibit good skill in three different time scales: (1) short-term (day-ahead); (2) immediate-short-term (6-h ahead); and (3) nowcasting (1-h ahead). The forecast method, which combined WRF/YSU/Noah outputs, persistence, and LS-SVM methods, increases the forecast skill by 26.3-49.4% compared to the direct outputs of numerical WRF/YSU/Noah model. Also, this approach captures well the diurnal cycle and seasonal variability of wind speeds, as well as wind direction.La estimación de la energía eólica está relacionada con la predicción en la variación de los vientos en pequeños intervalos de tiempo. Se seleccionaron cuatro torres eólicas ubicadas al interior de Mongolia para estudiar los recursos eólicos en la complejidad de un altiplano. Se utilizó la investigación climática a mesoscala y la combinación del esquema de la Universidad Yonsei con el Modelo de Superficie Terrestre Noah (WRF/YSU/Noah), con resolución de 1km horizontal y 10 minutos, como el modelo numérico de predicción meteorológica (NWP, del inglés Numerical Weather Prediction). Se utilizaron tres técnicas estadísticas, persistencia, propagación hacia atrás en redes neuronales artificiales y máquina de vectores de soporte-mínimos cuadrados (LS-SVM, del inglés Least Square Support Vector Machine), para mejorar la predicción de la velocidad del viento en una turbina con la altura del eje a 70 metros y se complementó con los resultados del WRF/YSU/Noah. Las técnicas de predicción físico-estadísticas actuales tienen un buen desempeo en tres escalas de tiempo: (1) corto plazo, un día en adelante; (2) mediano plazo, de seis días en adelante; (3) cercano, una hora en adelante. Este método de predicción, que combina los resultados WRF/YSU/Noah con los métodos de persistencia y LS-SVM incrementa la precisión de predicción entre 26,3 y 49,4 por ciento, comparado con los resultados directos del modelo numérico WRF/YSU/Noah. Además, este método diferencia la variabilidad de las estaciones y el ciclo diurno en la velocidad y la dirección del viento.application/pdfspaUniversidad Nacional de Colombia - Sede Bogotá - Facultad de Ciencias - Departamento de Geocienciahttps://revistas.unal.edu.co/index.php/esrj/article/view/63004Universidad Nacional de Colombia Revistas electrónicas UN Earth Sciences Research JournalEarth Sciences Research JournalDeng, Hua and Li, Yan and Zhang, Yingchao and Zhou, Hou and Cheng, Peipei and Wang, Jia and Ashraf, Muhammad Aqeel (2017) Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast. Earth Sciences Research Journal, 21 (1). pp. 37-43. ISSN 2339-345955 Ciencias de la tierra / Earth sciences and geologyWind forecastWRF/YSU/ NoahBP-ANNLS-SVMPredicción del vientoesquema de la Universidad Yonsei combinado con el Modelo de Superficie Terrestre Noah (WRF/YSU/Noah)propagación hacia atrás en redes neuronales artificialesmáquina de vectoresPrediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical ForecastArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINAL63004-327283-2-PB.pdfapplication/pdf1939713https://repositorio.unal.edu.co/bitstream/unal/63591/1/63004-327283-2-PB.pdfc3e235ae0ebbfe3473461331794dd639MD51THUMBNAIL63004-327283-2-PB.pdf.jpg63004-327283-2-PB.pdf.jpgGenerated Thumbnailimage/jpeg8148https://repositorio.unal.edu.co/bitstream/unal/63591/2/63004-327283-2-PB.pdf.jpgdc5c972d3a45e9c62557da4da2206630MD52unal/63591oai:repositorio.unal.edu.co:unal/635912023-04-22 23:05:37.42Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co |