基于蜣螂优化时间卷积网络的太阳辐照度预测算法

宋建材; 刘健蓉; 张鑫洋

doi:doi:10.19912/j.0254-0096.tynxb.2024-0228

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基于蜣螂优化时间卷积网络的太阳辐照度预测算法

更新时间：2025-12-02

- 基于蜣螂优化时间卷积网络的太阳辐照度预测算法
- Vol. 46, Issue 6, Pages: 494-500(2025)
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0228
  CLC： TU832
- Published Online：07 July 2025，
  
  Published：2025
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宋建材, 刘健蓉, 张鑫洋. 基于蜣螂优化时间卷积网络的太阳辐照度预测算法[J]. 2025, 46(6): 494-500.
DOI：

宋建材, 刘健蓉, 张鑫洋. 基于蜣螂优化时间卷积网络的太阳辐照度预测算法[J]. 2025, 46(6): 494-500. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0228.

摘要

针对现有机器学习预测算法超参数优化困难和精度难以满足优化调控需求的问题

提出一种基于蜣螂优化时间卷积网络（DBO-TCN）的太阳辐照度预测模型。该模型利用时间卷积网络（TCN）有效融合了卷积神经网络（CNN）的并行处理能力与递归神经网络（RNN）的时序建模功能

通过模拟蜣螂习性群智能优化算法对TCN的超参数进行优化

更加准确地挖掘太阳辐照度的时空演变规律。并与TCN、长短期记忆网络（LSTM）等主流预测算法进行详细对比实验

结果表明基于DBO-TCN提出的太阳辐照度预测模型具有更高的预测精度和鲁棒性。

Abstract

With the spatial and temporal characteristics of solar irradiance such as intermittency and nonlinearity

the prediction accuracy has a significant impact on the optimal operation of multi-energy cooperative heating systems. To address the problems of the existing machine learning prediction algorithms

such as the difficulty of hyper-parameter optimization and the insufficient accuracy to meet the demand of optimization and regulation

a solar irradiance prediction model based on the dung beetle optimized temporal convolutional network （DBO-TCN） is proposed. The model utilizes the TCN temporal convolutional network to effectively integrate the parallel processing capability of the convolutional neural network and the temporal modeling function of the recurrent neural network. The hyperparameters of TCN are optimized by simulating the dung beetle habit swarm intelligent optimization algorithm

which more accurately exploits the spatiotemporal evolution law of solar irradiance. Detailed comparison experiment results with state-of-the-art algorithms such as TCN and LSTM show that the proposed solar radiation prediction model based on DBO-TCN demonstrates superior prediction accuracy and robustness.

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