Short-term Wind Power Prediction Based on Dynamic Cluster Division and BLSTM Deep Learning Method

被引：0
|
作者：
Yang Z. [1 ]
Peng X. [1 ]
Lang J. [1 ]
Wang H. [1 ]
Wang B. [2 ]
Liu C. [2 ]
机构：
[1] State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan
[2] State Key Laboratory of Operation and Control of Renenable Energy & Storage Systems, China Electric Power Research Institute, Beijing
来源：
Gaodianya Jishu/High Voltage Engineering | 2021年 / 47卷 / 04期
基金：
国家重点研发计划;
关键词：
Cluster analysis; Cluster division; Deep learning; Neural network; Power prediction; Wind farm;
D O I：
10.13336/j.1003-6520.hve.20210079
中图分类号：
学科分类号：
摘要：
The wind power prediction for wind farm clusters is important for optimal scheduling of regional wind farms. The existing cluster prediction algorithms and cluster division methods do not consider the differential fluctuations among stations' numerical weather prediction (NWP) along time scale, and the cluster isn't divided reasonably. Consequently, we put for-ward a short-term wind power prediction method based on weather-process-dynamic wind farm cluster division. First, the 96-hour-time-scale prediction sample is equally divided into 16 sub-samples, followed by separate cluster division judging for each sub-sample. Then, the training set is organized for each sub-sample's sub-cluster according to cluster division result. Finally, the bidirectional long-short term memory (BLSTM) neural network is used for power prediction for each sub-cluster. The results show that,using the proposed method, the prediction accuracy can increase by 1.69%, 0.77%, and 0.59% compared to using the statistical upscaling method in 4 h-urtra-short-term, 24 h-day-ahead, and 96 h-short-term wind power prediction, respectively. The research can provide a reference for the topic of wind farm cluster division and short-term power prediction. © 2021, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
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页码：1195 / 1203
页数：8
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