基于降噪循环神经网络的风电功率预测

田增垚; 彭飞; 孟庆东; 王汉军; 田长翼; 陈志奎

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基于降噪循环神经网络的风电功率预测

更新时间：2026-09-04

- 基于降噪循环神经网络的风电功率预测
- Wind power prediction based on denoising recurrent neural network model
- 微电子学与计算机 2021年38卷第3期页码：27-32
- 作者机构：
  
  1. 国家电网公司东北分部,辽宁,沈阳,110180
  2. 中国科学院机构沈阳计算技术研究所有限公司，辽宁沈阳 1101681
  3. 大连理工大学软件学院,辽宁,大连,116620
- 作者简介：
- 基金信息：
  
  国家自然科学基金面上项目 61672123国家电网公司项目 26992618008K
- DOI：
  中图分类号：
- 纸质出版：2021
- 稿件说明：
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田增垚, 彭飞, 孟庆东, 王汉军, 田长翼, 陈志奎. 基于降噪循环神经网络的风电功率预测[J]. 微电子学与计算机, 2021, 38(3): 27-32.

TIAN Zeng-yao, PENG Fei, MENG Qing-dong, et al. Wind power prediction based on denoising recurrent neural network model[J]. 2021, 38(3): 27-32.
田增垚, 彭飞, 孟庆东, 王汉军, 田长翼, 陈志奎. 基于降噪循环神经网络的风电功率预测[J]. 微电子学与计算机, 2021, 38(3): 27-32. DOI：

TIAN Zeng-yao, PENG Fei, MENG Qing-dong, et al. Wind power prediction based on denoising recurrent neural network model[J]. 2021, 38(3): 27-32. DOI：

摘要

针对风电功率数据的时序性特点，提出降噪循环神经网络模型对电场中短期内的风电功率进行预测.通过模型能够挖掘其蕴含的知识，提高电力系统的稳定性，优化电力调度.模型首先采用循环神经网络构建一个编码-解码结构，设计编码器从序列变量中获取相应的深度特征，再通过解码器对深度特征进行解码，还原输入序列的状态并预测下一时刻的输出.进而，模型在解码器中设计降噪模块和预测模块，克服一般循环神经网络难以对带噪声数据进行预测的问题，使得模型能够对含有噪声的输入变量进行分析.通过利用电力物联网所采集的数据进行实验，结果证明提出的方法能很好地对风电功率进行预测，达到较好的预测效果

Abstract

Aiming at time series characteristics of wind power data

the Denoising Recurrent Neural Network model is proposed to predict the wind power in the short and medium term of the electric field. Through the model

the knowledge contained in it can be mined to improve the stability of the power system and optimize the power dispatching. An encoding-decoding structure is first designed in the recurrent neural network model

and the encoder is designed to obtain corresponding depth features from the sequence variables

and then the decoder decodes the depth features

restore the state of the input sequence and makes a prediction. Furthermore

the model designs the denoising module and prediction module in the decoder to overcome the difficulty of predicting noisy data with traditional recurrent neural networks

enabling the model to analyze input variables containing noise. By using the data collected by the power Internet of things to conduct experiments

the results show that the proposed method can forecast the wind power well and achieve a better prediction effect.

关键词

Keywords

references

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