Power data time sequence interpolation method and system based on improved TimesNet

Through the improved TimesNet model, combined with the Transformer-CNN hybrid model and deformable convolution kernel, the complex time dependence and periodic feature problems in power data missing value interpolation are solved, high-precision data interpolation is achieved, and the data support capability of the power system is improved.

CN120387130APending Publication Date: 2025-07-29国网福建省电力有限公司营销服务中心 +1
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Patent Information

Application Number
CN202510434094.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-08
Publication Date
2025-07-29

AI Technical Summary

Technical Problem

The prior art is difficult to effectively capture the complex time dependence and periodic characteristics in power data, resulting in inaccurate interpolation of missing data, affecting the intelligent scheduling and prediction accuracy of the power system.

Method used

The improved TimesNet model is adopted, combined with the Transformer-CNN hybrid model and deformable convolution kernel, through the axial attention mechanism and multi-scale feature extraction, dynamically adjust the receptive field, capture the local characteristics and periodic information of the power data, and improve the stability of the model through data augmentation technology.

Benefits of technology

It significantly improves the interpolation accuracy of power data missing values, enhances the stability of the model in noise and data missing scenarios, and improves the data support capabilities of the power system.

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Abstract

The invention relates to an improved TimesNet-based power data time sequence interpolation method and system. The method comprises the following steps of performing missing value identification, anomaly detection and standardized preprocessing on power data, and extracting time sequence features; according to the method, an improved TimesNet model is constructed, a 1D time sequence is converted into a 2D tensor, and features are extracted through a Transformer-CNN hybrid model: an axial attention branch establishes periodic internal and external dependencies in row and column directions, a multi-scale convolution branch captures local features, and a gating mechanism fuses output of the axial attention branch and the multi-scale convolution branch. A deformable convolution kernel is adopted to dynamically adjust a receptive field to adapt to a multi-scale time mode. And in training, time sequence enhancement and GAN are combined to generate data, so that the generalization of the model is improved. The method solves the problem that the traditional interpolation technology cannot effectively model the complex time sequence dependence of the electric power data, remarkably improves the missing value interpolation precision, and provides reliable data support for dispatching and prediction of an electric power system.
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