Power Load Curve Identification Method Based on Two-Phase Data Enhancement and Bi-directional Deep Residual TCN

doi:10.12204/j.issn.1000-7229.2022.02.011

Abstract

Abstract:

Deeply mining power consumer load law and perceiving electricity consumption behavior are of great significance for improving the service quality of power grid and the experience of power consumer. To deal with the issues of missing data, category imbalance and performance defects of classification model, this paper proposes a power user load curve classification method based on data enhancement and improved temporal convolution network (TCN). Firstly, a two-phase data enhancement method is proposed considering the global distribution characteristics of load data. In the first phase, the low rank tensor completion (LRTC) method based on tensor singular value threshold algorithm is introduced to complete the missing data. In the second phase, the generation adversarial network based on Wasserstein distance (WGAN) is used to enhance the minority samples to solve the problem of class imbalance. Secondly, a modified deep TCN classification model integrating bi-directional time-series features is constructed to realize accurate identification of large-scale power consumption curves. Finally, through the open-source criteria temporal dataset for classification and the actual load dataset, the proposed classification model shows better performance in convergence speed and classification accuracy, and the proposed data enhancement method can effectively improve the classification effect of models.

Key words: load classification, temporal convolutional network (TCN), generative adversarial network, low rank tensor completion, class imbalance, data missing

CLC Number:

TM714

ZHANG Jie, LIU Yang, LI Wenfeng, WANG Lei, XU Lixiong. Power Load Curve Identification Method Based on Two-Phase Data Enhancement and Bi-directional Deep Residual TCN[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(2): 89-97.

Figures/Tables 12

Fig.1 Schematic diagram of GAN

Fig.2 Temporal convolutional network

Fig.3 Deep residual blocks of TCN

Fig.4 Bi-DR-TCN model

Fig.5 Load classification framework

Table 1 Parameters of UCR data set

Table 2 Experiment results on UCR data

Fig.6 Comparison of training iterations

Fig.7 Comparison between missing and completion

Fig.8 Comparison between synthetic and real samples

Fig.9 Load classification results

Table 3 Comparison of different data enhancement methods

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