Detection of Non-Technical Losses Using SOSTLink and Bidirectional Gated Recurrent Unit to Secure Smart Meters

Clicks: 256
ID: 113959
2020
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #74 of 187 articles by views in applied sciences

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 187 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Energy consumption is increasing exponentially with the increase in electronic gadgets. Losses occur during generation, transmission, and distribution. The energy demand leads to increase in electricity theft (ET) in distribution side. Data analysis is the process of assessing the data using different analytical and statistical tools to extract useful information. Fluctuation in energy consumption patterns indicates electricity theft. Utilities bear losses of millions of dollar every year. Hardware-based solutions are considered to be the best; however, the deployment cost of these solutions is high. Software-based solutions are data-driven and cost-effective. We need big data for analysis and artificial intelligence and machine learning techniques. Several solutions have been proposed in existing studies; however, low detection performance and high false positive rate are the major issues. In this paper, we first time employ bidirectional Gated Recurrent Unit for ET detection for classification using real time-series data. We also propose a new scheme, which is a combination of oversampling technique Synthetic Minority Oversampling TEchnique (SMOTE) and undersampling technique Tomek Link: “Smote Over Sampling Tomik Link (SOSTLink) sampling technique”. The Kernel Principal Component Analysis is used for feature extraction. In order to evaluate the proposed model’s performance, five performance metrics are used, including precision, recall, F1-score, Root Mean Square Error (RMSE), and receiver operating characteristic curve. Experiments show that our proposed model outperforms the state-of-the-art techniques: logistic regression, decision tree, random forest, support vector machine, convolutional neural network, long short-term memory, hybrid of multilayer perceptron and convolutional neural network.
Reference Key
joshi2020applieddetection Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hira Gul,Nadeem Javaid,Ibrar Ullah,Ali Mustafa Qamar,Muhammad Khalil Afzal,Gyanendra Prasad Joshi;Hira Gul;Nadeem Javaid;Ibrar Ullah;Ali Mustafa Qamar;Muhammad Khalil Afzal;Gyanendra Prasad Joshi;
Journal applied sciences
Year 2020
DOI
10.3390/app10093151
URL
https://sciprofiles.com/publication/view/1abb5e9b0a9981a8a4721fe3b5124af5
Keywords
artificial intelligence Neural network electricity theft smart meter supervised learning advance meter infrastructure non technical losses gated recurrent unit artificial intelligence Neural network electricity theft smart meter supervised learning advance meter infrastructure non technical losses gated recurrent unit

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.