Short-term wind speed forecasting using recurrent neural networks with error correction
文献类型：期刊论文
| 作者 | Duan, Jikai2; Zuo, Hongchao2; Bai, Yulong3; Duan, Jizheng1; Chang, Mingheng2; Chen, Bolong2 |
| 刊名 | ENERGY
 |
| 出版日期 | 2021-02-15 |
| 卷号 | 217页码:16 |
| 关键词 | Wind speed forecasting Recurrent neural network ARIMA |
| ISSN号 | 0360-5442 |
| DOI | 10.1016/j.energy.2020.119397 |
| 通讯作者 | Zuo, Hongchao(zuohch@lzu.edu.cn) |
| 英文摘要 | As a type of clean energy, wind energy has been effectively used in power systems. However, due to the influence of the atmospheric boundary layer, wind speed exhibits strong nonlinearity and nonstationarity. Therefore, the accurate and stable prediction of wind speed is highly important for the security of the power grid. To improve the forecasting accuracy, a novel hybrid forecasting system is proposed in this paper that includes effective data decomposition techniques, recurrent neural network prediction algorithms and error decomposition correction methods. In this system, a novel decomposition approach is used to first decompose the original wind speed series into a set of subseries, then it predicts the wind speed by recurrent neural network, and finally, it decomposes the error to correct the previously predicted wind speed. The effectiveness of the proposed model is verified using data from four different wind farms in China. The results show that the proposed hybrid system is superior to other single models and traditional models and realizes highly accurate prediction of wind speed. The proposed system may be a useful tool for smart grid operation and management. (C) 2020 Elsevier Ltd. All rights reserved. |
| WOS关键词 | EMPIRICAL MODE DECOMPOSITION ; EXTREME LEARNING-MACHINE ; POWER ; ALGORITHM ; EMD ; OPTIMIZATION ; STRATEGY |
| 资助项目 | National Natural Science Foundation of China[41875009] ; National Natural Science Foundation of China[41861047] |
| WOS研究方向 | Thermodynamics ; Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:000611852800014 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://119.78.100.186/handle/113462/137885]  |
| 专题 | 中国科学院近代物理研究所 |
| 通讯作者 | Zuo, Hongchao |
| 作者单位 | 1.Chinese Acad Sci, Inst Modern Phys, Lanzhou 730000, Peoples R China 2.Lanzhou Univ, Coll Atmospher Sci, Lanzhou 730000, Peoples R China 3.Northwest Normal Univ Lanzhou, Coll Phys & Elect Engn, Lanzhou 730070, Peoples R China |
| 推荐引用方式 GB/T 7714 | Duan, Jikai,Zuo, Hongchao,Bai, Yulong,et al. Short-term wind speed forecasting using recurrent neural networks with error correction[J]. ENERGY,2021,217:16. |
| APA | Duan, Jikai,Zuo, Hongchao,Bai, Yulong,Duan, Jizheng,Chang, Mingheng,&Chen, Bolong.(2021).Short-term wind speed forecasting using recurrent neural networks with error correction.ENERGY,217,16. |
| MLA | Duan, Jikai,et al."Short-term wind speed forecasting using recurrent neural networks with error correction".ENERGY 217(2021):16. |
入库方式： OAI收割
来源：近代物理研究所
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