基于自回归与高频动态耦合模型的日典型特征光伏功率4~168 h短中期多步预测

黄晶; 袁程旭; 郭苏

doi:doi:10.19912/j.0254-0096.tynxb.2024-0277

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基于自回归与高频动态耦合模型的日典型特征光伏功率4~168 h短中期多步预测

更新时间：2025-12-02

- 基于自回归与高频动态耦合模型的日典型特征光伏功率4~168 h短中期多步预测
- Vol. 46, Issue 6, Pages: 298-305(2025)
- 作者机构：
- 作者简介：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0277
  CLC： TM615
- Published Online：07 July 2025，
  
  Published：2025
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黄晶, 袁程旭, 郭苏. 基于自回归与高频动态耦合模型的日典型特征光伏功率4~168 h短中期多步预测[J]. 2025, 46(6): 298-305.
DOI：

黄晶, 袁程旭, 郭苏. 基于自回归与高频动态耦合模型的日典型特征光伏功率4~168 h短中期多步预测[J]. 2025, 46(6): 298-305. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0277.

摘要

提出一种基于自回归与高频动态耦合模型的日典型特征多步预测方法。对光伏出力数据与气象信息进行相关性及数理统计分析

建立日典型特征矩阵

通过日典型特征矩阵对自回归与高频动态耦合模型进行修正

实现短期（4 h-16步、24 h-96步）

中期（168 h-168步）多步预测

结果表明该模型具有较高的预测精度

相比于长短期记忆网络（LSTM）误差降低26.1~57.8个百分点

其中提前7 d预测的全年NRMSE为21.8%。

Abstract

A daily typical characteristics multi-step prediction method based on a coupled autoregressive and dynamical system model is proposed. The correlation between PV output data and weather information is analyzed and examined

and the matrix of daily typical characteristics is established. Then

through the matrix information of daily typical characteristics

the forecasting value of coupled autoregressive and dynamical system model is fixed as exponential smoothing to cuhieve short-term （16 steps in 4 hours

96 steps in 24 hours）

medium-term （168 hours 168 steps） multi-step prediction. Comparing with the popular LSTM model

the results show that the model has a high prediction accuracy and the error is reduced by 26.1-57.8 percentage points. Of this

the full year NRMSE value predicted 7 days in advance is 21.8%.

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references

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