Wind Power Prediction Method Based on Long Short-term Memory Neural Network

Xiangjun LI; Gejian XU

doi:10.12096/j.2096-4528.pgt.19108

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Wind Power Prediction Method Based on Long Short-term Memory Neural Network

New and Renewable Energy | 更新时间：2025-06-06

- Wind Power Prediction Method Based on Long Short-term Memory Neural Network
- Power Generation Technology Vol. 40, Issue 5, Pages: 426-433(2019)
- 作者机构：
  
  新能源与储能运行控制国家重点实验室(中国电力科学研究院有限公司), 北京市海淀区 100192
- 作者简介：
- 基金信息：
  
  Science and Technology Project of State Grid Corporation of China(DG71-19-015)
- DOI：10.12096/j.2096-4528.pgt.19108
  CLC：
- Received：16 July 2019，
  
  Published：30 October 2019
- 稿件说明：
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Xiangjun LI, Gejian XU. Wind Power Prediction Method Based on Long Short-term Memory Neural Network[J]. Power Generation Technology, 2019, 40(5): 426-433.
DOI：

Xiangjun LI, Gejian XU. Wind Power Prediction Method Based on Long Short-term Memory Neural Network[J]. Power Generation Technology, 2019, 40(5): 426-433. DOI： 10.12096/j.2096-4528.pgt.19108.

摘要

风力发电过程具有较强的随机性，导致风力发电功率的预测准确度不高。针对上述问题，提出了一种融合深度学习算法的风力发电功率预测方法。以历史风力发电功率数据作为输入，建立风力发电功率预测模型，实现对未来一个时间刻度的风力发电功率预测。算例结果表明，与传统时序预测方法相比，基于长短期记忆神经网络的风力发电功率预测结果在各项指标中误差更小，验证了上述方法在风力发电功率预测中的可行性和有效性，提升了风力发电功率预测的准确性。

Abstract

Wind power generation process has strong randomness

which leads to low accuracy of wind power prediction. In view of the above phenomenon

a wind power generation power prediction method based on deep learning algorithm was proposed. Taking the historical wind power data as input

a wind power prediction model was established to realize the wind power prediction on a time scale in the future. The results of the example show that compared with the traditional time-series prediction method

the average absolute error of the wind power prediction results based on long short-term memory neural network is smaller in each index

which verifies the feasibility and effectiveness of the above method in wind power generation prediction

and improves the accuracy of wind power generation prediction.

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Keywords

references

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