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Deep learning-based electricity theft prediction in non-smart grid environments

In developing countries, smart grids are nonexistent, and electricity theft significantly hampers power supply. This research introduces a lightweight deep-learning model using monthly customer readings as input data. By employing careful direct and indirect feature engineering techniques, including...

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Principais autores: Sheikh Muhammad Saqib, Tehseen Mazhar, Muhammad Iqbal, Tariq Shahazad, Ahmad Almogren, Khmaies Ouahada, Habib Hamam
Formato: Artigo
Idioma:Inglês
Publicado: Elsevier 2024-08-01
Series:Heliyon
Assuntos:
Deep learning
Feature engineering
Principal Component Analysis (PCA)
t-distributed Stochastic Neighbor Embedding (t-SNE)
Random-Under-Sampler (RUS)
Synthetic Minority Over-Sampling Technique (SMOTE)
Acceso en liña:http://www.sciencedirect.com/science/article/pii/S240584402411198X
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