EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid

• URL: http://arxiv.org/abs/2110.04502v1
• Date: Sat, 9 Oct 2021 08:19:03 GMT
• Title: EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid
• Authors: Yogesh Kulkarni, Sayf Hussain Z, Krithi Ramamritham, Nivethitha Somu
• Abstract summary: We present EnsembleNTLDetect, a robust and scalable electricity theft detection framework. It employs a set of efficient data pre-processing techniques and machine learning models to accurately detect electricity theft. A Conditional Generative Adversarial Network (CTGAN) is used to augment the dataset to ensure robust training.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Artificial intelligence-based techniques applied to the electricity consumption data generated from the smart grid prove to be an effective solution in reducing Non Technical Loses (NTLs), thereby ensures safety, reliability, and security of the smart energy systems. However, imbalanced data, consecutive missing values, large training times, and complex architectures hinder the real time application of electricity theft detection models. In this paper, we present EnsembleNTLDetect, a robust and scalable electricity theft detection framework that employs a set of efficient data pre-processing techniques and machine learning models to accurately detect electricity theft by analysing consumers' electricity consumption patterns. This framework utilises an enhanced Dynamic Time Warping Based Imputation (eDTWBI) algorithm to impute missing values in the time series data and leverages the Near-miss undersampling technique to generate balanced data. Further, stacked autoencoder is introduced for dimensionality reduction and to improve training efficiency. A Conditional Generative Adversarial Network (CTGAN) is used to augment the dataset to ensure robust training and a soft voting ensemble classifier is designed to detect the consumers with aberrant consumption patterns. Furthermore, experiments were conducted on the real-time electricity consumption data provided by the State Grid Corporation of China (SGCC) to validate the reliability and efficiency of EnsembleNTLDetect over the state-of-the-art electricity theft detection models in terms of various quality metrics.

Related papers

• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning [97.99077847606624]
  This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making.
  arXiv  Detail & Related papers  (2024-04-02T10:19:04Z)
• An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids [0.0]
  This paper proposes an unsupervised adversarial autoencoder (AAE) model to detect false data injection attacks (FDIAs) in unbalanced power distribution grids. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements. It is tested on IEEE 13-bus and 123-bus systems with historical meteorological data and historical real-world load data.
  arXiv  Detail & Related papers  (2024-03-31T01:20:01Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Low-Frequency Load Identification using CNN-BiLSTM Attention Mechanism [0.0]
  Non-intrusive Load Monitoring (NILM) is an established technique for effective and cost-efficient electricity consumption management. This paper presents a hybrid learning approach, consisting of a convolutional neural network (CNN) and a bidirectional long short-term memory (BILSTM) CNN-BILSTM model is adept at extracting both temporal (time-related) and spatial (location-related) features, allowing it to precisely identify energy consumption patterns at the appliance level.
  arXiv  Detail & Related papers  (2023-11-14T21:02:27Z)
• Hybrid Transformer-RNN Architecture for Household Occupancy Detection Using Low-Resolution Smart Meter Data [8.486902848941872]
  Digitalization of the energy system provides smart meter data that can be used for occupancy detection in a non-intrusive manner. Deep learning techniques make it possible to infer occupancy from low-resolution smart meter data. Our work is motivated to develop a privacy-aware and effective model for residential occupancy detection.
  arXiv  Detail & Related papers  (2023-08-27T14:13:29Z)
• A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics [56.86150790999639]
  We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer. The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
  arXiv  Detail & Related papers  (2022-09-23T06:09:35Z)
• Ranking-Based Physics-Informed Line Failure Detection in Power Grids [66.0797334582536]
  Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. This paper proposes a Physics-InformEd Line failure Detector (FIELD) that leverages grid topology information to reduce sample and time complexities and improve localization accuracy.
  arXiv  Detail & Related papers  (2022-08-31T18:19:25Z)
• Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels [52.60092598312894]
  We propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes. Our proposed solution results in minimal energy consumption and $CO$ emission compared to the considered baselines.
  arXiv  Detail & Related papers  (2021-06-02T07:36:27Z)
• An Ensemble Deep Convolutional Neural Network Model for Electricity Theft Detection in Smart Grids [2.281079191664481]
  Electricity Theft Detection (EDT) algorithms are typically used for such purpose since this Non-Technical Loss (NTL) may lead to significant challenges in the power system. In this paper, an Ensemble Deep Convolutional Neural Network (EDCNN) algorithm for ETD in smart grids has been proposed.
  arXiv  Detail & Related papers  (2021-02-10T18:27:13Z)
• Artificial Intelligence based Sensor Data Analytics Framework for Remote Electricity Network Condition Monitoring [0.0]
  Rural electrification demands the use of inexpensive technologies such as single wire earth return (SWER) networks. There is a steadily growing energy demand from remote consumers, and the capacity of existing lines may become inadequate soon. High impedance arcing faults (HIF) from SWER lines can cause catastrophic bushfires such as the 2009 Black Saturday event.
  arXiv  Detail & Related papers  (2021-01-21T07:50:01Z)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.