A hybrid strategy of short term wind power prediction

Huaiwu Peng, Fangrui Liu and Xiaofeng Yang

Renewable Energy, 2013, vol. 50, issue C, 590-595

Abstract: Two different prediction methods are investigated for short term wind power prediction of a wind farm in this paper. The adopted strategies are individual artificial neural network (ANN) and hybrid strategy based on the physical and the statistical methods. The performance of two prediction methods is comprehensively compared. The calculated results show that the individual ANN prediction method can yield the prediction results quickly. The prediction accuracy is low and the root mean squared error (RMSE) is 10.67%. By contrast the hybrid prediction method operates costly and slowly. However, the prediction accuracy is high and the RMSE is 2.01%, less than 1/5 of that by individual ANN method. Meanwhile, it is found that the errors of the prediction have some relation with the wind speeds. The prediction errors are small when the wind speeds lower than 5 m/s or higher than 15 m/s. The reasons for such phenomena are also investigated.

Keywords: Short term wind speed prediction; Artificial neural network; Hybrid prediction (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:50:y:2013:i:c:p:590-595

DOI: 10.1016/j.renene.2012.07.022

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