基于TCN-NCP-CFC神经网络的超短期风电功率预测

赵飞; 吴文标; 汪燕毅

doi:doi:10.19912/j.0254-0096.tynxb.2024-2249

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基于TCN-NCP-CFC神经网络的超短期风电功率预测

更新时间：2026-07-22

- 基于TCN-NCP-CFC神经网络的超短期风电功率预测
- Vol. 47, Issue 4, Pages: 376-386(2026)
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-2249
  CLC： TM614
- Online First：08 May 2026，
  
  Published：2026
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赵飞, 吴文标, 汪燕毅. 基于TCN-NCP-CFC神经网络的超短期风电功率预测[J]. 2026, 47(4): 376-386.
DOI：

赵飞, 吴文标, 汪燕毅. 基于TCN-NCP-CFC神经网络的超短期风电功率预测[J]. 2026, 47(4): 376-386. DOI： doi:10.19912/j.0254-0096.tynxb.2024-2249.

摘要

针对风力发电功率的不确定性和波动性，提出一种结合时间卷积网络（TCN）、闭式连续时间网络（CFC）和神经回路策略（NCP）的超短期风电功率预测方法。首先，使用TCN对原始数据进行初步学习，提取时间序列中的关键信息；随后，将其输入NCP-CFC网络中，利用NCP独特的分层类脑递归连接结构及CFC高效的求解机制和防梯度消失特性进行预测；最后，通过全连接层调整输出范围和维度，获得最终预测结果。通过模型结构的消融实验以及与RNN类模型的对比实验验证各模块的必要性。通过两组算例分析（内蒙古某风电场的算例：MSE=25.70 MW2、RMSE=5.07 MW、MAE=3.92 MW、SMAPE=32.51%、R2=0.92；开源算例：MSE=27.38 MW2、RMSE=5.23 MW、MAE=3.71 MW、SMAPE=38.52%、R2=0.84），验证了所提模型在超短期风电功率预测中的有效性。

Abstract

To address the uncertainties and fluctuations associated with wind power generation

an ultra-short-term wind power forecasting method integrating temporal convolutional networks （TCN）

closed-form continuous-time networks （CFC）

and neural circuit policies （NCP） is proposed. Initially

TCN is utilized to conduct preliminary learning from raw data

extracting crucial information from the time series. Subsequently

the processed data is fed into the NCP-CFC network

which leverages the unique hierarchical brain-like recursive connections of NCP and the efficient solving mechanism and anti-gradient vanishing properties of CFC for forecasting. Finally

a fully connected layer adjusts the output range and dimensions to produce the final forecast. The necessity of each module is validated through ablation studies and comparative experiments with RNN-based models. Two case studies are conducted to demonstrate the effectiveness of the proposed model in ultra-short-term wind power forecasting： one involving a wind farm in Inner Mongolia （MSE=25.70 MW

RMSE=5.07 MW

MAE=3.92 MW

SMAPE=32.51%

=0.92） and another using open-source data （MSE=27.38 MW

RMSE=5.23 MW

MAE=3.71 MW

SMAPE=38.52%

=0.84）.

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references

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