An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
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Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
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Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 3 | Issue - 4 | december 2021
Published
22 November, 2021
In power systems, electrical losses can be categorized into two types, namely, Technical Losses (TLs) and Non-Technical Losses (NTLs). It has been identified that NTL is more hazardous when compared to TL, primarily due to the factors such as billing errors, faulty meters, electricity theft etc. This proves to be crucial in the power system and will result in heavy financial loss for the utility companies. To identify theft, both academia and industry, use a mechanism known as Electricity Theft Detection (ETD). However, ETD is not used efficiently because of handling high-dimensional data, overfitting issues and imbalanced data. Hence, in this paper, a means of addressing this issue using Random Under-Sampling Boosting (RUSBoost) technique and Long Short-Term Memory (LSTM) technique is proposed. Here, parameter optimization is performed using RUSBoost and abnormal electricity patterns are detected by LSTM technique. Electricity data are pre-processed in the proposed methodology, using interpolation and normalization methods. The data thus obtained are then sent to the LSTM module where feature extraction takes place. These features are then classified using RUSBoost algorithm. Based on the output simulated, it is identified that this methodology addresses several issues such as handling and overfitting of massive time series data and data imbalancing. Moreover, this technique also proves to be more efficient than several other methodologies such as Logistic Regression (LR), Convolutional Neural Network (CNN) and Support Vector Machine (SVM). A comparison is also drawn, taking into consideration the parameters such as Receiver operating characteristics, recall, precision and F1-score.
KeywordsParameter Tuning Imbalanced data Random under sampling Smart meter Electricity theft non-technical losses Long Short-Term Memory RUSBoost
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