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

YANG Zimin; PENG Xiaosheng; LANG Jianxun; WANG Hongyu; WANG Bo; LIU Chun

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

更新时间：2025-12-08

- Short-term Wind Power Prediction Based on Dynamic Cluster Division and BLSTM Deep Learning Method
- Vol. 47, Issue 4, Pages: 1195-1203(2021)
- 作者机构：
  
  1. 华中科技大学电气与电子工程学院强电磁工程与新技术国家重点实验室
  2. 中国电力科学研究院有限公司新能源与储能运行控制国家重点实验室
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：30 April 2021，
  
  Published：2021
- 稿件说明：
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YANG Zimin, PENG Xiaosheng, LANG Jianxun, et al. Short-term Wind Power Prediction Based on Dynamic Cluster Division and BLSTM Deep Learning Method[J]. 2021, 47(4): 1195-1203.
DOI：

YANG Zimin, PENG Xiaosheng, LANG Jianxun, et al. Short-term Wind Power Prediction Based on Dynamic Cluster Division and BLSTM Deep Learning Method[J]. 2021, 47(4): 1195-1203. DOI：

摘要

风电集群的整体功率预测对区域风电的优化调度具有重要意义

现有集群预测方法并未考虑集群内各风电场数值天气预报（numerical weather prediction

NWP）信息在时间序列上的差异性波动

并按此进行集群的合理划分。为此

提出了基于天气过程动态划分的风电集群短期功率预测方法。首先将96 h时间尺度的待预测样本均分成16份等时长的子样本;然后对每份子样本分别进行集群的聚类与划分;再依据划分结果构建各子样本所含子集群的训练集;最后通过双向长短期记忆（bidirectional long short-term memory

BLSTM）人工神经网络对各子集群进行功率预测。算例结果表明

所提方法在4 h超短期预测、24 h日前预测、96 h短期预测中相较统计升尺度法可分别提高1.69%、0.77%和0.59%的精度。论文研究可为风电集群划分和短期功率预测提供参考。

Abstract

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 forward 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.

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