Electricity Theft Detection Using Interpretable IGANN

Generated from prompt:

Create a modern, visually engaging presentation (clean minimal design, icons, section dividers) based on the paper: "Interpretable generalized additive neural network for electricity theft detection in smart cities using balanced data and intelligent grid management". Slides: 1. Title – Electricity Theft Detection using IGANN 2. Introduction – Smart cities, rising demand, theft problem 3. Problem Statement – Imbalanced data, low accuracy, lack of interpretability 4. Proposed Framework – IGANN-ETD-SCBD-IGM overview diagram style 5. Pipeline – Data acquisition → RN-SMOTE → RLKF → MSGVT → IGANN → SBOA 6. Data Balancing – RN-SMOTE explanation (visual bullets) 7. Preprocessing – RLKF (missing values, outliers, normalization) 8. Feature Extraction – Entropy, Mean, Variance, Contrast, Correlation, Kurtosis 9. Model – IGANN (interpretable neural network) 10. Optimization – SBOA (nature-inspired optimization) 11. Results – Accuracy 99%, Precision 94.04%, Recall 93.5% 12. Comparison – vs KNN, CNN, SVM (highlight improvement) 13. Confusion Matrix Insight – high TP, low FN 14. Explainability – SHAP insights (feature importance) 15. Real-world impact – smart grids, reduced losses 16. Conclusion – benefits and effectiveness 17. Future Work – scalability, real-time deployment Style: - Dark gradient + neon accents - Icons for each step - Minimal text per slide - Bold headings - Clean charts placeholders Output as a polished presentation.

This presentation outlines the IGANN-ETD-SCBD-IGM framework for detecting electricity theft in smart grids. It addresses imbalanced data, noisy inputs, and black-box models through RN-SMOTE balancing, RLKF preprocessing, MSGVT features, IGANN neural,

April 27, 202617 slides

Slide 1 of 17

Slide 1 - Title Slide

Electricity Theft Detection using IGANN

Using Interpretable Generalized Additive Neural Networks for Smart City Security

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Photo by Taylor R on Unsplash

Slide 2 of 17

Slide 2 - Introduction: The Smart Grid Challenge

Smart cities enable efficient urban living through interconnected grids.
Rising global energy demand mandates reliable monitoring.
Electricity theft remains a significant financial and operational challenge for utility providers.

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Photo by SiravitPlug on Unsplash

Slide 2 - Introduction: The Smart Grid Challenge

Slide 3 of 17

Slide 3 - Problem Statement

Imbalanced Datasets: Electricity theft cases are rare compared to normal usage.
Low Model Accuracy: Conventional methods struggle with high-dimensional, noisy data.
Black-Box Models: Difficulty in interpreting AI decisions limits real-world adoption.

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Photo by Point3D Commercial Imaging Ltd. on Unsplash

Slide 4 of 17

Slide 4 - Proposed Framework Overview

Proposed Framework: IGANN-ETD-SCBD-IGM.
Integrates balancing, feature engineering, and neural modeling.
Designed for high accuracy and transparent decision-making.

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Photo by Point3D Commercial Imaging Ltd. (https://unsplash.com/@3dottawa?utmsource=karaf&utmmedium=referral) on Unsplash (https://unsplash.com/?utmsource=karaf&utmmedium=referral) Photo by Shubham Dhage on Unsplash

Slide 5 of 17

Slide 5 - The Detection Pipeline

Phase	Step
Acquisition	Data Collection
Balancing	RN-SMOTE
Preprocessing	RLKF Imputation
Extraction	MSGVT Feature Engineering
Modeling	IGANN Architecture
Optimizing	SBOA Optimization

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Slide 6 of 17

Slide 6 - Data Balancing: RN-SMOTE

RN-SMOTE: Robust Neighborhood Synthetic Minority Over-sampling Technique.
Addresses class imbalance by generating synthetic samples for the minority class.
Ensures training data reflects real-world theft patterns accurately.

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Slide 7 of 17

Slide 7 - Preprocessing with RLKF

RLKF: Robust Local Kalman Filter.
Handles missing values effectively.
Removes noisy outliers.
Applies normalization for consistent input scaling.

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Photo by Valentin Lacoste on Unsplash

Slide 8 of 17

Slide 8 - Feature Extraction (MSGVT)

📊 Entropy Measure of randomness/irregularity.

📈 Mean Average energy consumption.

📉 Variance Fluctuation intensity.

🧩 Contrast Texture of power signals.

🔗 Correlation Dependency between metrics.

🔔 Kurtosis Shape of distribution tail.

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Slide 9 of 17

Slide 9 - Model: IGANN Architecture

IGANN: Interpretable Generalized Additive Neural Network.
Combines the predictive power of neural networks with the transparency of additive models.
Allows users to understand exactly how features influence theft detection.

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Photo by Valentin Lacoste on Unsplash