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

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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Bibliographic Details
Main Authors: Ting Yang, Hongyi Yu, Danhong Lu, Shengkui Bai, Yan Li, Wenyao Fan, Ketian Liu
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Energies
Subjects:
abnormal power load data
GAN–transformer
one-hot encoding
anomaly detection
anomaly classification
Online Access:https://www.mdpi.com/1996-1073/18/5/1062
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