基于IDBO优化的BiTCN-BiLSTM-SA光伏组件积灰预测

冯平平; 文桥; 王霄; 杨靖; 王涛; 徐凌桦

doi:doi:10.19912/j.0254-0096.tynxb.2024-1244

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基于IDBO优化的BiTCN-BiLSTM-SA光伏组件积灰预测

更新时间：2025-12-10

- 基于IDBO优化的BiTCN-BiLSTM-SA光伏组件积灰预测
- Issue 11, Pages: 319-329(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1244
  CLC： M615
- Published Online：04 December 2025，
  
  Published：04 December 2025
- 稿件说明：
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冯平平, 文桥, 王霄, et al. 基于IDBO优化的BiTCN-BiLSTM-SA光伏组件积灰预测[J]. 2025, (11): 319-329.
DOI：

冯平平, 文桥, 王霄, et al. 基于IDBO优化的BiTCN-BiLSTM-SA光伏组件积灰预测[J]. 2025, (11): 319-329. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1244.

摘要

提出一种基于改进蜣螂优化算法（IDBO）的双向时间卷积网络（BiTCN）和双向长短期记忆网络（BiLSTM）结合自注意力机制（SA）的光伏组件积灰预测模型

用于对光伏组件透光率进行有效预测

再基于透光率与积灰密度的拟合公式

实现光伏组件的积灰预测。预测模型利用BiTCN提取局部特征、BiLSTM捕捉全局时间依赖关系

并通过SA优化特征权重分配

以提高预测精度。同时

使用IDBO优化模型超参数

进一步提升预测性能。通过搭建物联网采集平台获取光伏组件透光率及相关环境数据制作数据集用于模型仿真分析。实验结果显示

所提模型的均方根误差为0.0126

平均绝对误差为0.007

预测精度达到96.26%

整体优于其他算法

提升了光伏组件积灰预测的精度。

Abstract

This study proposes an innovative dust accumulation prediction model for photovoltaic （PV） modules through the integration of an improved dung beetle optimization algorithm （IDBO） with a hybrid neural network architecture combining a bidirectional temporal convolutional network （BiTCN）

a bidirectional long short-term memory （BiLSTM）

and self-attention （SA）. The developed model achieves accurate prediction of PV module transmittance

subsequently enabling dust density estimation through established transmittance-dust density correlation formulas. The architecture strategically employs BiTCN for local feature extraction

BiLSTM for capturing global temporal dependencies

and SA for dynamic feature weighting optimization

thereby enhancing overall prediction precision. Furthermore

the IDBO algorithm is implemented for hyperparameter optimization to maximize model performance. To validate the approach

we establish an IoT-based data acquisition platform that collects comprehensive PV module transmittance data and associated environmental parameters

forming a robust dataset for model training and evaluation. Experimental results demonstrate superior performance metrics with root mean square error （RMSE） of 0.0126

mean absolute error （MAE） of 0.007

and prediction accuracy reaching 96.26%

significantly outperforming benchmark algorithms. This advancement enables more precise dust accumulation forecasting.

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