Detection and Classification of Abnormal Power Load Data by Combining One-Hot Encoding and GAN–Transformer
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/5/1062 |
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| author | Ting Yang Hongyi Yu Danhong Lu Shengkui Bai Yan Li Wenyao Fan Ketian Liu |
| author_facet | Ting Yang Hongyi Yu Danhong Lu Shengkui Bai Yan Li Wenyao Fan Ketian Liu |
| author_sort | Ting Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a2e27bdabffc42a2bc83966cb6eda536 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-a2e27bdabffc42a2bc83966cb6eda5362025-08-20T02:05:09ZengMDPI AGEnergies1996-10732025-02-01185106210.3390/en18051062Detection and Classification of Abnormal Power Load Data by Combining One-Hot Encoding and GAN–TransformerTing Yang0Hongyi Yu1Danhong Lu2Shengkui Bai3Yan Li4Wenyao Fan5Ketian Liu6School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaThe 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.https://www.mdpi.com/1996-1073/18/5/1062abnormal power load dataGAN–transformerone-hot encodinganomaly detectionanomaly classification |
| spellingShingle | Ting Yang Hongyi Yu Danhong Lu Shengkui Bai Yan Li Wenyao Fan Ketian Liu Detection and Classification of Abnormal Power Load Data by Combining One-Hot Encoding and GAN–Transformer Energies abnormal power load data GAN–transformer one-hot encoding anomaly detection anomaly classification |
| title | Detection and Classification of Abnormal Power Load Data by Combining One-Hot Encoding and GAN–Transformer |
| title_full | Detection and Classification of Abnormal Power Load Data by Combining One-Hot Encoding and GAN–Transformer |
| title_fullStr | Detection and Classification of Abnormal Power Load Data by Combining One-Hot Encoding and GAN–Transformer |
| title_full_unstemmed | Detection and Classification of Abnormal Power Load Data by Combining One-Hot Encoding and GAN–Transformer |
| title_short | Detection and Classification of Abnormal Power Load Data by Combining One-Hot Encoding and GAN–Transformer |
| title_sort | detection and classification of abnormal power load data by combining one hot encoding and gan transformer |
| topic | abnormal power load data GAN–transformer one-hot encoding anomaly detection anomaly classification |
| url | https://www.mdpi.com/1996-1073/18/5/1062 |
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