Electricity Theft Detection with self-attention
This addresses electricity theft detection for utility companies, representing an incremental advance in applying attention mechanisms to this domain-specific problem.
The paper tackles electricity theft detection on an imbalanced real-world dataset by proposing a novel self-attention mechanism model, achieving an AUC of 0.926, which is a 17% improvement over previous baselines.
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.