article · Sensors
Genetic Algorithms are combined with the XGBoost machine learning framework to carry out hyperparameter optimisation specifically for fraud detection in smart grid environments. Empirical evaluations reveal that optimising the model yields marked performance enhancements, most notably lifting classification accuracy from 0.82 to 0.978. Alongside accuracy, clear gains are achieved across precision, recall, and the area under the receiver operating characteristic curve metrics, confirming the effectiveness of this tuning strategy. The results demonstrate the practical value of applying advanced metaheuristic algorithms to refine complex predictive models. Overall, the methodology represents clear progress in advancing both the operational accuracy and efficiency of automated fraud detection mechanisms designed for smart electricity grids.
Fraud and illicit electricity consumption present serious operational and financial challenges to power networks. Using advanced optimisation techniques to dramatically improve model accuracy helps smart grid operators detect fraudulent anomalies more reliably. Higher precision and recall reduce costly errors, supporting the development of dependable automated monitoring systems for modern energy distribution networks.
The primary application is automated fraud and theft detection software for smart electricity grids, aimed at utility companies and grid managers. The work represents applied and tested algorithmic research, having demonstrated validated metric improvements in testing. Moving towards real-world deployment would require incorporating the tuned machine learning pipeline into live smart meter data streams and utility control centres.
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This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The empirical findings demonstrate a noteworthy enhancement in the model's performance metrics following optimization, particularly emphasizing a substantial increase in accuracy from 0.82 to 0.978. The precision, recall, and AUROC metrics demonstrate a clear improvement, indicating the effectiveness of optimizing the XGBoost model for fraud detection. The findings from our study significantly contribute to the expanding field of smart grid fraud detection. These results emphasize the potential uses of advanced metaheuristic algorithms to optimize complex machine-learning models. This work showcases significant progress in enhancing the accuracy and efficiency of fraud detection systems in smart grids.
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DOI: 10.3390/s24041230
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