Developing and Implementing an Artificial Intelligence (AI)-Driven System For Electricity Theft Detection
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Abstract
Electricity theft is a significant challenge for utility companies worldwide, leading to substantial economic losses and inefficiencies in power distribution. Traditional methods of detecting electricity theft, such as manual inspections and routine audits, are often inefficient and ineffective. To address this issue, this study aims to develop and implement an artificial intelligence (AI)-driven system for electricity theft detection. Methodology used are data collection, data analysis, feature selection with Chi-Square, feature transformation with Principal Component Analysis (PCA), Support Vector Machine (SVM) and model for electricity theft detection. To achieve this, a Particle Swarm Optimization Algorithm (PSO) was applied to improve training performance of the SVM, using data of meter recharge information collected from Enugu Electricity Distribution Company (EEDC). The system effectiveness is validated through extensive testing using real-world data from various regions and scenarios, demonstrating its robustness and adaptability. The system result considering FDR reported that 0.11 was achieved for the particle swarm based SVM model. When TPR was considered for analysis, it was observed that particle swarm based SVM attained a score of 0.89. In addition, Particle swarm based SVM attained PPV of 0.895. In terms of accuracy, the particle swarm based SVM reported an accuracy of 0.857. The result showed that the particle swarm based SVM performed better from the system validation achieved through comparative analysis, hence it is recommended for use to develop the new software for energy theft investigation. The implementation of this AI-driven solution offers numerous benefits, including enhanced detection accuracy, reduced operational costs, and improved overall efficiency of power distribution networks. Moreover, it enables utility companies to take proactive measures to prevent theft, ensuring a more reliable and secure electricity supply for consumers.
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References
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