Cable temperature prediction model based on multi-scale patch and convolution interaction

doi:10.11772/j.issn.1001-9081.2025010122

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (1): 314-321.DOI: 10.11772/j.issn.1001-9081.2025010122

• Frontier and comprehensive applications • Previous Articles Next Articles

Cable temperature prediction model based on multi-scale patch and convolution interaction

Tingting WANG¹, Tingshun LI¹(), Wen TAN², Bo LYU¹, Yixuan CHEN¹

^1.School of Control and Computer Engineering，North China Electric Power University，Beijing 102206，China
^2.School of Electrical and Control Engineering，North China University of Technology，Beijing 100144，China

Received:2025-02-07 Revised:2025-03-27 Accepted:2025-03-28 Online:2026-01-10 Published:2026-01-10
Contact: Tingshun LI
About author:WANG Tingting， born in 2000， M. S. candidate. Her research interests include cable temperature data mining， deep learning.
TAN Wen， born in 1968， Ph. D.， professor. His research interests include linear active disturbance rejection control and automation.
LYU Bo， born in 1998， M. S. candidate. His research interests include computer vision， data augmentation.
CHEN Yixuan， born in 2000， M. S. candidate. His research interests include natural language processing.
Supported by:
National Natural Science Foundation of China(61573138)

基于多尺度Patch与卷积交互的电缆温度预测模型

王婷婷¹, 李廷顺¹(), 谭文², 吕博¹, 陈翼轩¹

^1.华北电力大学控制与计算机工程学院，北京 102206
^2.北方工业大学电气与控制工程学院，北京 100144

通讯作者: 李廷顺
作者简介:王婷婷（2000—），女，河北邯郸人，硕士研究生，主要研究方向：电缆温度数据挖掘、深度学习
谭文（1968—），男，江西九江人，教授，博士，主要研究方向：线性自抗扰控制和自动化
吕博（1998—），男，黑龙江齐齐哈尔人，硕士研究生，主要研究方向：计算机视觉、数据增强
陈翼轩（2000—），男，北京人，硕士研究生，主要研究方向：自然语言处理。
基金资助:
国家自然科学基金资助项目(61573138)

Abstract

Abstract:

Prolonged overheating of high-voltage cables may lead to insulation thermal breakdown， consequently affecting the stability of the power grid. However， the existing research primarily focuses on traditional prediction models， and ignores the complexity and dynamic characteristics of temperature data. To address this limitation， a cable temperature prediction model based on Multi-Scale Patch and Convolution Interaction （MSP-CI） was proposed. Firstly， the input dimension was reduced using a channel resampling method， and a multi-scale patch branch structure was constructed， so as to decouple the complex time series. Then， macroscopic information from coarse-grained patches and microscopic information from fine-grained patches were extracted， respectively， through the combination of sequence decomposition and convolution interaction strategies. Finally， an attention fusion module was constructed to balance the weights of macroscopic and microscopic information dynamically and obtain the final prediction results. Experimental results on real high-voltage cable temperature datasets demonstrate that compared to the baseline models such as TimeMixer， PatchTST （Patch Time Series Transformer）， and MSGNet （Multi-Scale inter-series Graph Network）， MSP-CI achieves a reduction of 7.02% to 34.87% in Mean Squared Error （MSE）， and a reduction of 5.15% to 32.04% in Mean Absolute Error （MAE）. It can be seen that MSP-CI enhances cable temperature prediction accuracy effectively， providing a reliable basis for power dispatching operations.

Key words: high-voltage cable, temperature prediction, multi-scale, patch, convolutional interaction

摘要：

高压电缆长期过热可能导致绝缘热击穿，进而影响电网的稳定性。然而，当前研究主要集中在传统预测模型上，忽略了温度数据的复杂性和动态特征。为了解决此问题，提出一种基于多尺度Patch与卷积交互的电缆温度预测模型（MSP-CI）。首先，采用通道重组采样方法降低输入维度，并构建多尺度Patch分支结构，以实现复杂时间序列的解耦；其次，结合序列分解与卷积交互策略，分别提取粗粒度Patch的宏观信息与细粒度Patch的微观信息；最后，构建注意力融合模块，以动态平衡宏观与微观信息的权重，并得到最终的预测结果。在真实高压电缆温度数据集上的实验结果表明， MSP-CI相较于TimeMixer、PatchTST （Patch Time Series Transformer）和MSGNet（Multi-Scale inter-series Graph Network）等基线模型，在均方误差（MSE）上下降了7.02%~34.87%，在平均绝对误差（MAE）上下降了5.15%~32.04%。可见， MSP-CI能有效提升电缆温度预测的准确率，为电力调度运行提供依据。

关键词: 高压电缆, 温度预测, 多尺度, patch, 卷积交互

CLC Number:

TM18

Tingting WANG, Tingshun LI, Wen TAN, Bo LYU, Yixuan CHEN. Cable temperature prediction model based on multi-scale patch and convolution interaction[J]. Journal of Computer Applications, 2026, 46(1): 314-321.

王婷婷, 李廷顺, 谭文, 吕博, 陈翼轩. 基于多尺度Patch与卷积交互的电缆温度预测模型[J]. 《计算机应用》唯一官方网站, 2026, 46(1): 314-321.

Figures/Tables 13

Fig. 1 High-voltage cable fiber optic temperature measurement device

Fig. 2 Model architecture and prediction flow

Fig. 3 Temperature curves of some measuring points

Fig. 4 Structure of ?、ρ、ψ and η

Fig. 5 Structure of depthwise separable convolution

Tab. 1 Experimental hyperparameter setting

参数	HVCT1	HVCT2
batch_size	32	32
lr	0.001	0.001
r_steps	103	286
epoch	10	10
e_layers	2	2
M	4	2

Fig. 6 Prediction results on HVCT1 varying with r_steps

Fig. 7 Prediction results on HVCT2 varying with r_steps

Tab. 2 Comparison of experimental results

数据集	预测长度	本文模型		PatchTST		TimeMixer		MSGNet		xPatch		iTransformer		DLinear		Autoformer		MICN		STHD
数据集	预测长度	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
HVCT1	48	0.220	0.339	0.240	0.350	0.259	0.354	0.249	0.352	0.243	0.353	0.283	0.364	0.282	0.371	0.337	0.395	0.284	0.364	0.299	0.394
	96	0.274	0.370	0.301	0.389	0.277	0.372	0.281	0.380	0.299	0.385	0.308	0.381	0.289	0.376	0.362	0.412	0.302	0.385	0.321	0.400
	192	0.293	0.381	0.349	0.408	0.328	0.400	0.339	0.405	0.345	0.419	0.330	0.395	0.335	0.400	0.384	0.432	0.334	0.405	0.335	0.435
	平均	0.262	0.363	0.297	0.382	0.288	0.375	0.290	0.379	0.296	0.386	0.307	0.380	0.302	0.382	0.361	0.413	0.307	0.385	0.318	0.410
HVCT2	48	0.485	0.632	0.503	0.674	0.518	0.704	0.515	0.712	0.512	0.699	0.546	0.774	0.509	0.698	0.637	0.994	0.620	0.688	0.807	0.914
	96	0.512	0.673	0.544	0.737	0.537	0.721	0.545	0.739	0.547	0.740	0.575	0.896	0.553	0.753	0.677	1.385	0.644	0.718	0.861	0.972
	192	0.536	0.696	0.568	0.744	0.575	0.819	0.57	0.828	0.573	0.750	0.584	0.998	0.583	0.803	0.806	1.787	0.672	0.748	0.990	1.229
	平均	0.511	0.667	0.538	0.718	0.543	0.748	0.543	0.760	0.544	0.730	0.568	0.889	0.548	0.751	0.707	1.389	0.645	0.718	0.886	1.038

Tab. 3 Ablation study results

模型	MSE	MAE
w/o MSP	0.255	0.354
w/o AF	0.261	0.352
Model-1	0.295	0.374
Model-2	0.304	0.401
Model-3	0.241	0.350
本文模型	0.237	0.342

Fig. 8 Prediction results for different lookback windows L

Tab. 4 Multi-scale patch combination results

M	$p 1, p 2, …, p M$	MSE	MAE
1	｛16｝	0.267	0.363
1	｛32｝	0.288	0.373
2	｛16，8｝	0.263	0.356
2	｛32，16｝	0.277	0.367
3	｛16，8，4｝	0.257	0.357
3	｛32，16，8｝	0.255	0.354
4	｛32，16，8，4｝	0.251	0.355
4	｛64，32，16，8｝	0.237	0.343
5	｛64，32，16，8，4｝	0.264	0.360
5	｛128，64，32，16，8｝	0.245	0.355

Tab. 4 Multi-scale patch combination results

M	$p 1, p 2, …, p M$	MSE	MAE
1	｛16｝	0.267	0.363
1	｛32｝	0.288	0.373
2	｛16，8｝	0.263	0.356
2	｛32，16｝	0.277	0.367
3	｛16，8，4｝	0.257	0.357
3	｛32，16，8｝	0.255	0.354
4	｛32，16，8，4｝	0.251	0.355
4	｛64，32，16，8｝	0.237	0.343
5	｛64，32，16，8，4｝	0.264	0.360
5	｛128，64，32，16，8｝	0.245	0.355

Fig. 9 Visualized results of different models

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Cable temperature prediction model based on multi-scale patch and convolution interaction

基于多尺度Patch与卷积交互的电缆温度预测模型

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