# Electricity Theft Detection Techniques

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/electricity-theft-detection-techniques/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the detection and prevention of electricity theft in smart grids, particularly through the use of advanced metering infrastructure, machine learning, deep learning, and anomaly detection techniques. The research explores methods such as support vector machines, decision trees, convolutional neural networks, and feature engineering to address non-technical losses and improve the security of electricity distribution systems. |
| Domain | Physical Sciences |
| Field | Engineering |
| OpenAlex ID | t13429 |
| Works | 26 |

## Topic papers all

Showing 15 of 26.

- [A survey on Advanced Metering Infrastructure](https://scholariq.org/papers/a-survey-on-advanced-metering-infrastructure/)
- [A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining](https://scholariq.org/papers/a-comparison-of-undersampling-oversampling-and-smote-methods-for-dealing-with/)
- [Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques](https://scholariq.org/papers/addressing-binary-classification-over-class-imbalanced-clinical-datasets-using/)
- [Federated learning model for credit card fraud detection with data balancing techniques](https://scholariq.org/papers/federated-learning-model-for-credit-card-fraud-detection-with-data-balancing/)
- [Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects](https://scholariq.org/papers/machine-learning-scopes-on-microgrid-predictive-maintenance-potential-frameworks/)
- [Deep learning for intelligent demand response and smart grids: A comprehensive survey](https://scholariq.org/papers/deep-learning-for-intelligent-demand-response-and-smart-grids-a-comprehensive/)
- [Ensemble Bagged Tree Based Classification for Reducing Non-Technical Losses in Multan Electric Power Company of Pakistan](https://scholariq.org/papers/ensemble-bagged-tree-based-classification-for-reducing-non-technical-losses-in/)
- [Few-shot imbalanced classification based on data augmentation](https://scholariq.org/papers/few-shot-imbalanced-classification-based-on-data-augmentation/)
- [Machine learning approach of detecting anomalies and forecasting time-series of IoT devices](https://scholariq.org/papers/machine-learning-approach-of-detecting-anomalies-and-forecasting-time-series-of/)
- [Short-Term Electricity-Load Forecasting by deep learning: A comprehensive survey](https://scholariq.org/papers/short-term-electricity-load-forecasting-by-deep-learning-a-comprehensive-survey/)
- [Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review](https://scholariq.org/papers/detection-of-non-technical-losses-in-power-utilities-a-comprehensive-systematic/)
- [Performance of Machine Learning Algorithms for Class-Imbalanced Process Fault Detection Problems](https://scholariq.org/papers/performance-of-machine-learning-algorithms-for-class-imbalanced-process-fault/)
- [Multi-Stage Prediction for Zero-Inflated Hurricane Induced Power Outages](https://scholariq.org/papers/multi-stage-prediction-for-zero-inflated-hurricane-induced-power-outages/)
- [Constrained Oversampling: An Oversampling Approach to Reduce Noise Generation in Imbalanced Datasets With Class Overlapping](https://scholariq.org/papers/constrained-oversampling-an-oversampling-approach-to-reduce-noise-generation-in/)
- [A Novel Multitier Blockchain Architecture to Protect Data in Smart Metering Systems](https://scholariq.org/papers/a-novel-multitier-blockchain-architecture-to-protect-data-in-smart-metering/)

## Topic primary papers

- [Ensemble Bagged Tree Based Classification for Reducing Non-Technical Losses in Multan Electric Power Company of Pakistan](https://scholariq.org/papers/ensemble-bagged-tree-based-classification-for-reducing-non-technical-losses-in/)
- [Machine learning approach of detecting anomalies and forecasting time-series of IoT devices](https://scholariq.org/papers/machine-learning-approach-of-detecting-anomalies-and-forecasting-time-series-of/)
- [Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review](https://scholariq.org/papers/detection-of-non-technical-losses-in-power-utilities-a-comprehensive-systematic/)
- [Data-driven intelligent method for detection of electricity theft](https://scholariq.org/papers/data-driven-intelligent-method-for-detection-of-electricity-theft/)
- [A multi-tier architecture for data analytics in smart metering systems](https://scholariq.org/papers/a-multi-tier-architecture-for-data-analytics-in-smart-metering-systems/)
- [Multi-scale DenseNet-Based Electricity Theft Detection](https://scholariq.org/papers/multi-scale-densenet-based-electricity-theft-detection/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.