Energy theft detection in an edge data center using threshold-based abnormality detector

•Framework includes VAE-GAN training, and threshold-based detector formulation.•The VAE-GAN has good convergence performance and grasps the statistical properties.•The proposed feature extraction can learn data representation well.•The proposed detector can achieve higher accuracy with lower computational burden.•The proposed energy theft detector is robust against attack type changes. With the fast development of industrial Internet of Things (IoT) for smart energy, data processing and storing are closer to the end used side. Edge data center, an intermediate platform between end data source and centralized data center, can reduce the data transmission pressure and processing time. To provide dependable data source for decision making and to reduce property loss, energy theft detection is important to an edge data center. In this work, we propose a threshold-based abnormality detector for energy theft detection in an edge data center. The framework includes training feature extractor based on VAE-GAN, implementing k-means clustering to determine the representative features of normal load profiles, and finally formulating a threshold-based abnormality detector based on defined abnormality degree. We demonstrate that when VAE-GAN converges, it can grasp the temporal relationship and statistical distribution of real data. The encoder of VAE-GAN has good feature extraction performance and the distribution of normal and abnormal data can be easily separated. Also, we prove that the proposed feature representation is better than the feature extracted by other advanced feature extractors. By comparison with state-of-the-art detection models, the proposed detector is more computationally efficient and robust against the attack type changes.