Detection and Classification of Abnormal Power Load Data by Combining One-Hot Encoding and GAN–Transformer

Ting Yang, Hongyi Yu, Danhong Lu, Shengkui Bai, Yan Li, Wenyao Fan and Ketian Liu ()
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Ting Yang: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Hongyi Yu: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Danhong Lu: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Shengkui Bai: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Yan Li: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Wenyao Fan: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Ketian Liu: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China

Energies, 2025, vol. 18, issue 5, 1-19

Abstract: The explosive growth of power load data has led to a substantial presence of abnormal data, which significantly reduce the accuracy of power system operation planning, load forecasting, and energy usage analysis. To address this issue, a novel improved GAN–Transformer model is proposed, leveraging the adversarial structure of the generator and discriminator in Generative Adversarial Networks (GANs). To provide the model with a suitable feature dataset, One-hot encoding is introduced to label different categories of abnormal power load data, enabling staged mapping and training of the model with the labeled dataset. Experimental results demonstrate that the proposed model accurately identifies and classifies mutation anomalies, sustained extreme anomalies, spike anomalies, and transient extreme anomalies. Furthermore, it outperforms traditional methods such as LSTM-NDT, Transformer, OmniAnomaly and MAD-GAN in Overall Accuracy, Average Accuracy, and Kappa coefficient, thereby validating the effectiveness and superiority of the proposed anomaly detection and classification method.

Keywords: abnormal power load data; GAN–transformer; one-hot encoding; anomaly detection; anomaly classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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