A power quality time-series forecasting method based on the TimesNet-Transformer fusion model

WEI Kangyao; HAO Yuanyu; YANG Dongdong; LI Dan; WANG Jiale; ZHENG Lijun

doi:10.19783/j.cnki.pspc.260161

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A power quality time-series forecasting method based on the TimesNet-Transformer fusion model

更新时间：2026-07-27

- A power quality time-series forecasting method based on the TimesNet-Transformer fusion model
- Power System Protection and Control Vol. 54, Issue 13, (2026)
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- DOI：10.19783/j.cnki.pspc.260161
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- Published：2026
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WEI Kangyao, HAO Yuanyu, YANG Dongdong, et al. A power quality time-series forecasting method based on the TimesNet-Transformer fusion model[J]. Power System Protection and Control, 2026, 54(13).
DOI：

WEI Kangyao, HAO Yuanyu, YANG Dongdong, et al. A power quality time-series forecasting method based on the TimesNet-Transformer fusion model[J]. Power System Protection and Control, 2026, 54(13). DOI： 10.19783/j.cnki.pspc.260161.

摘要

为解决传统深度学习模型难以充分挖掘电能质量数据中复杂时间特征的问题，提出一种基于 TimesNet-Transformer 融合模型的预测方法。该方法首先利用 TimesNet 中的时序二维变换模块，将一维时间序列转换为二维空间结构，提取复杂时间特征。其次，引入多层 Transformer 网络，捕捉特征中的长期依赖关系。然后，设计自适应加权融合模块，对两个分支所提取的特征进行对齐与加权融合，强化关键信息，并通过全连接层输出最终的预测结果。最后，以山西省太原市某地区电网全年实际运行数据构建数据集进行验证。实验结果表明，所提方法在模型计算精度上较 Transformer 和 TimesNet 分别提升 11.83% 和 3.16%，训练效率提升约 29.32%。欧洲公开数据集跨域泛化验证显示，融合模型在核心电能质量指标上均取得最优精度，验证了其在复杂电网场景下的适应性与鲁棒性。

Abstract

To address the challenges that traditional deep learning models struggle to fully extract the complex temporal features from power quality data

a forecasting method based on the TimesNet-Transformer hybrid model is proposed. The proposed method first uses the temporal two-dimensional transformation module in TimesNet to convert one-dimensional time-series data into a two-dimensional spatial structure for extracting complex temporal feature vectors. Second

a multi-layer Transformer network is introduced to capture long-term dependencies within these features. Then

an adaptive weighted fusion module is designed to align and integrate the features extracted by the two branches

thereby enhancing critical information representation and generating the final forecasting results through a fully connected layer. Finally

a dataset constructed from a full year of actual operational data collected from a regional power grid in Taiyuan

Shanxi Province

is used for validation. Experimental results show that the proposed method achieves an average improvement of 11.83% and 3.16% in model computation accuracy compared with Transformer and TimesNet

respectively

while increasing training efficiency by approximately 29.32%. Cross-domain generalization validation on a European public dataset further demonstrates that the hybrid model achieves the highest prediction accuracy across key power quality indicators

confirming its adaptability and robustness under complex power grid scenarios.

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references

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