Multivariate Time Series Analysis for Electrical Power Theft Detection in the Distribution Grid

The challenge of energy theft is one of the most critical issues for distribution network operators. In addition to causing significant economic losses, theft can compromise the quality of electricity supply, lead to blackouts, and slow down the transition to sustainable energy. With the introduction of smart grids and advanced meters, it is now possible to tackle this issue using cutting-edge artificial intelligence technologies. This innovative study proposes a system based on deep neural networks, such as CNNs, to detect energy theft through the analysis of multivariate time series, demonstrating how AI can revolutionize the automatic detection of anomalies.

The developed system leverages CNNs to analyze data collected in real-world scenarios, such as industrial sites with verified theft incidents. The data, acquired from multiple sensors, include variables such as cumulative active and reactive energy, total monthly power, and average power calculated across different time slots. These datasets, manually labeled based on certified reports, enable the model to identify anomalies in consumption and pinpoint the moments when theft occurs. Thanks to its multivariate structure, the system can uncover hidden correlations among variables, providing a more comprehensive view compared to traditional methods.

The system was tested on five years of data, encompassing consumption profiles of users with 548 sequences sampled monthly. With an average accuracy of 76.1% in binary classification and 78.4% in a multiclass problem, the model has proven to be reliable and robust. Despite the complexity of the analyzed sequences and the variability of the case studies, the CNN effectively distinguished between regular and irregular consumption, significantly reducing error margins.

This technology is not limited to detecting theft; it marks a significant step toward more efficient and resilient management of distribution networks. By automating traditionally slow and costly processes, such as manual meter inspections, the system helps reduce economic and energy losses. Additionally, the scalability of the model makes it suitable for diverse contexts, from urban to industrial areas, seamlessly integrating with modern smart grid infrastructures.