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Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm
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DOI：10.1007/s40565-015-0171-6.png)
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Abstract
En 中文
Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy, the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accuracy. To improve forecasting accuracy, this paper focuses on two aspects: (1)proposing a novel hybrid method using Boosting algorithm and a multi-step forecast approach to improve the forecasting capacity of traditional ARMA model; (2)calculating the existing error bounds of the proposed method. To validate the effectiveness of the novel hybrid method, one-year period of real data are used for test, which were collected from three operating wind farms in the east coast of Jiangsu Province, China. Meanwhile conventional ARMA model and persistence model are both used as benchmarks with which the proposed method is compared. Test results show that the proposed method achieves a more accurate forecast.
Keywords:
Hybrid method
Multi-step-ahead prediction
Wind power forecast
Boosting algorithm
Time series model
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