Electricity Theft Detection with self-attention

Abstract

In this work we propose a novel self-attention mechanism model to address electricity theft detection on an imbalanced realistic dataset that presents a daily electricity consumption provided by State Grid Corporation of China. Our key contribution is the introduction of a multi-head self-attention mechanism concatenated with dilated convolutions and unified by a convolution of kernel size $1$. Moreover, we introduce a binary input channel (Binary Mask) to identify the position of the missing values, allowing the network to learn how to deal with these values. Our model achieves an AUC of $0.926$ which is an improvement in more than $17\%$ with respect to previous baseline work. The code is available on GitHub at https://github.com/neuralmind-ai/electricity-theft-detection-with-self-attention.

References in corpus (6)

• Scikit-learn: Machine Learning in Python
• Multi-Scale Context Aggregation by Dilated Convolutions
• Fully Convolutional Networks for Semantic Segmentation
• The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
• Missing Data Prediction and Classification: The Use of Auto-Associative Neural Networks and Optimization Algorithms
• Multi-Scale DenseNet-Based Electricity Theft Detection

Cited by in corpus (1)

• Normalizador Neural de Datas e Endereços