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Related Experiment Video

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection.

Adil Mehdary¹, Abdellah Chehri², Abdeslam Jakimi³

¹LaGes, Hassania School of Public Works, Casablanca 20000, Morocco.

Sensors (Basel, Switzerland)

|February 24, 2024

Summary

This summary is machine-generated.

Genetic Algorithms (GA) optimized XGBoost for smart grid fraud detection, significantly boosting accuracy from 0.82 to 0.978. This enhances the efficiency and reliability of detecting fraudulent activities in smart grids.

Keywords:

SGCC dataset XGBoost electricity theft fraud detection genetic algorithms hyperparameter optimization metaheuristic algorithms smart grids

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Area of Science:

Computer Science
Electrical Engineering
Data Science

Background:

Smart grids are increasingly vulnerable to sophisticated fraud.
Existing fraud detection models often require significant performance tuning.
Hyperparameter optimization is crucial for machine learning model efficacy.

Purpose of the Study:

To optimize the XGBoost model for enhanced fraud detection in smart grids.
To evaluate the impact of Genetic Algorithms (GA) on XGBoost hyperparameter tuning.
To improve the accuracy and efficiency of smart grid fraud detection systems.

Main Methods:

Utilized Genetic Algorithms (GA) for hyperparameter optimization of the XGBoost model.
Applied the optimized XGBoost model to a smart grid fraud detection dataset.
Evaluated model performance using metrics such as accuracy, precision, recall, and AUROC.

Main Results:

Achieved a substantial increase in model accuracy from 0.82 to 0.978 post-optimization.
Demonstrated significant improvements in precision, recall, and AUROC metrics.
Validated the effectiveness of GA-driven hyperparameter tuning for XGBoost in this context.

Conclusions:

The combination of Genetic Algorithms and XGBoost offers a powerful approach for smart grid fraud detection.
Optimized XGBoost models exhibit superior performance in identifying fraudulent activities.
This research contributes to advancing the security and integrity of smart grid infrastructure.