A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning
文献类型：期刊论文
| 作者 | Wang, Y. S.1,2,3,4; Gao, J.1,2; Xu, Z. W.3,4,5; Luo, J. D.6; Li, L. X.3,4 |
| 刊名 | INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
 |
| 出版日期 | 2020-08-01 |
| 卷号 | 15期号:4页码:18 |
| 关键词 | wind power output power ultra-short-term prediction deep learning (DL) long short-term memory (LSTM) model |
| ISSN号 | 1841-9836 |
| DOI | 10.15837/ijccc.2020.4.3901 |
| 英文摘要 | The output power prediction of wind farm is the key to effective utilization of wind energy and reduction of wind curtailment. However, the prediction of output power has long been a difficulty faced by both academia and the wind power industry, due to the high stochasticity of wind energy. This paper attempts to improve the ultra-short-term prediction accuracy of output power in wind farm. For this purpose, an output power prediction model was constructed for wind farm based on the time sliding window (TSW) and long short-term memory (LSTM) network. Firstly, the wind power data from multiple sources were fused, and cleaned through operations like dimension reduction and standardization. Then, the cyclic features of the actual output powers were extracted, and used to construct the input dataset by the TSW algorithm. On this basis, the TSW-LSTM prediction model was established to predict the output power of wind farm in ultra-short-term. Next, two regression evaluation metrics were designed to evaluate the prediction accuracy. Finally, the proposed TSW-LSTM model was compared with four other models through experiments on the dataset from an actual wind farm. Our model achieved a super-high prediction accuracy 92.7% as measured by d_MAE, an evidence of its effectiveness. To sum up, this research simplifies the complex prediction features, unifies the evaluation metrics, and provides an accurate prediction model for output power of wind farm with strong generalization ability. |
| 资助项目 | Inner Mongolia Science and Technology Major Special Projects[2019ZD016] ; Natural Science Foundation of China[61462070] ; Natural Science Foundation of China[61962045] ; Natural Science Foundation of China[61502255] ; Natural Science Foundation of China[61650205] ; Inner Mongolia Agricultural University Doctoral Scientific Research Fund Project[BJ09-44] ; Natural Science Foundation of Inner Mongolia Autonomous Region[2019MS03014] ; Natural Science Foundation of Inner Mongolia Autonomous Region[2018MS-06003] ; Natural Science Foundation of Inner Mongolia Autonomous Region[2019MS06027] ; Inner Mongolia Key Technological Development Program[2019ZD015] ; Key Scientific and Technological Research Program of Inner Mongolia Autonomous Region[2019GG273] |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000540296700008 |
| 出版者 | CCC PUBL-AGORA UNIV |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/15234]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Gao, J. |
| 作者单位 | 1.Inner Mongolia Autonomous Reg Key Lab Big Data Re, Hohhot 010018, Peoples R China 2.Inner Mongolia Agr Univ, Coll Comp & Informat Engn, Hohhot 010018, Peoples R China 3.Inner Mongolia Univ Technol, Coll Data Sci & Applicat, Hohhot 010080, Peoples R China 4.Inner Mongolia Autonomous Reg Engn & Technol Res, Hohhot 010080, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China 6.Haohan Data Technol Co Ltd, Beijing 100080, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Y. S.,Gao, J.,Xu, Z. W.,et al. A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning[J]. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL,2020,15(4):18. |
| APA | Wang, Y. S.,Gao, J.,Xu, Z. W.,Luo, J. D.,&Li, L. X..(2020).A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning.INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL,15(4),18. |
| MLA | Wang, Y. S.,et al."A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning".INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL 15.4(2020):18. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。