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Open AccessArticle

A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

by Yiqi Chu 1, Chengcai Li 1,*, Yefang Wang 1,2, Jing Li 1 and Jian Li 1
1
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
2
Baicheng Ordnance Test Center of China, Baicheng 137001, China
*
Author to whom correspondence should be addressed.
Academic Editor: José C. Riquelme
Energies 2016, 9(11), 894; https://doi.org/10.3390/en9110894
Received: 5 August 2016 / Revised: 14 October 2016 / Accepted: 20 October 2016 / Published: 31 October 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF) model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28–52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction. View Full-Text
Keywords: wind power; wind forecasting; statistical correction; weather classification; ensemble forecasting wind power; wind forecasting; statistical correction; weather classification; ensemble forecasting
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MDPI and ACS Style

Chu, Y.; Li, C.; Wang, Y.; Li, J.; Li, J. A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction. Energies 2016, 9, 894.

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