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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wu Junli Liu Xingjie Qian Jian |
| Copyright Year | 2010 |
| Description | Author affiliation: Bayin Wind Farm, Longyuan Baotou Wind Power Co., Ltd., China (Qian Jian) || Department of Electrical Engineering, North China Electric Power University, Baoding, China (Wu Junli; Liu Xingjie) |
| Abstract | As a renewable and clean energy source, wind power is being widely utilized all over the world. The uncertainty of wind speed, however, makes certain trouble for the development of wind power generation. In order to relieve the disadvantageous impact of wind speed intermittence on the connected power system, the wind power forecasting needs to be carried out. In this paper, a wind speed and power forecasting method based on RBF neural network is proposed. In which, the influence of the dataset construct method on the forecasting accuracy is researched. The simulation results show that the forecasting accuracy is improved by performing the dataset reconstruction. And it is proved that the higher forecasting accuracy of wind power can be gotten through introducing the wind speed as RBF inputs. |
| File Size | 598453 |
| File Format | |
| ISBN | 9781424472352 |
| e-ISBN | 9781424472376 |
| DOI | 10.1109/ICCASM.2010.5619079 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-10-22 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Wavelet transforms Accuracy RBF neural network Dataset reconstruction Wind speed Predictive models Wind farms Power systems Wind power Forecasting |
| Content Type | Text |
| Resource Type | Article |
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