Published

2017-01-01

Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast

Predicción de vientos en una altiplanicie a la altura del eje con el esquema de la Universidad Yonsei/Modelo Superficie Terrestre Noah y la predicción estadística

DOI:

https://doi.org/10.15446/esrj.v21n1.63004

Keywords:

Wind forecast, WRF/YSU/ Noah, BP-ANN, LS-SVM (en)
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 (es)

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Authors

  • Hua Deng Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing 210044, China
  • Yan Li Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing 210044, China
  • Yingchao Zhang Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing 210044, China
  • Hou Zhou Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • Peipei Cheng Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • Jia Wang Inner Mongolia Meteorological Service Center, Huhehaote 010051, China
  • Muhammad Aqeel Ashraf Faculty of Science and Natural Resources, University Malaysia Sabah 88400 Kota Kinabalu Sabah, Malaysia

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.

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How to Cite

APA

Deng, H., Li, Y., Zhang, Y., Zhou, H., Cheng, P., Wang, J. and Ashraf, M. A. (2017). Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast. Earth Sciences Research Journal, 21(1), 37–43. https://doi.org/10.15446/esrj.v21n1.63004

ACM

[1]
Deng, H., Li, Y., Zhang, Y., Zhou, H., Cheng, P., Wang, J. and Ashraf, M.A. 2017. Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast. Earth Sciences Research Journal. 21, 1 (Jan. 2017), 37–43. DOI:https://doi.org/10.15446/esrj.v21n1.63004.

ACS

(1)
Deng, H.; Li, Y.; Zhang, Y.; Zhou, H.; Cheng, P.; Wang, J.; Ashraf, M. A. Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast. Earth sci. res. j. 2017, 21, 37-43.

ABNT

DENG, H.; LI, Y.; ZHANG, Y.; ZHOU, H.; CHENG, P.; WANG, J.; ASHRAF, M. A. Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast. Earth Sciences Research Journal, [S. l.], v. 21, n. 1, p. 37–43, 2017. DOI: 10.15446/esrj.v21n1.63004. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/63004. Acesso em: 14 jan. 2025.

Chicago

Deng, Hua, Yan Li, Yingchao Zhang, Hou Zhou, Peipei Cheng, Jia Wang, and Muhammad Aqeel Ashraf. 2017. “Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast”. Earth Sciences Research Journal 21 (1):37-43. https://doi.org/10.15446/esrj.v21n1.63004.

Harvard

Deng, H., Li, Y., Zhang, Y., Zhou, H., Cheng, P., Wang, J. and Ashraf, M. A. (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. doi: 10.15446/esrj.v21n1.63004.

IEEE

[1]
H. Deng, “Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast”, Earth sci. res. j., vol. 21, no. 1, pp. 37–43, Jan. 2017.

MLA

Deng, H., Y. Li, Y. Zhang, H. Zhou, P. Cheng, J. Wang, and M. A. Ashraf. “Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast”. Earth Sciences Research Journal, vol. 21, no. 1, Jan. 2017, pp. 37-43, doi:10.15446/esrj.v21n1.63004.

Turabian

Deng, Hua, Yan Li, Yingchao Zhang, Hou Zhou, Peipei Cheng, Jia Wang, and Muhammad Aqeel Ashraf. “Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast”. Earth Sciences Research Journal 21, no. 1 (January 1, 2017): 37–43. Accessed January 14, 2025. https://revistas.unal.edu.co/index.php/esrj/article/view/63004.

Vancouver

1.
Deng H, Li Y, Zhang Y, Zhou H, Cheng P, Wang J, Ashraf MA. Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast. Earth sci. res. j. [Internet]. 2017 Jan. 1 [cited 2025 Jan. 14];21(1):37-43. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/63004

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