Identifying Nontechnical Power Loss via Spatial and Temporal Deep Learning

Bhat, Rajendra R.; Trevizan, Rodrigo D.; Sengupta, Rahul; Li, Xiaolin; Bretãs, Arturo S.

doi:10.1109/icmla.2016.0052

Cited by 72 publications

(51 citation statements)

References 21 publications

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“…Hartmann et al 2015of Bhat et al (2016) have tested convolutional neural network, auto encoders and long short-term memory networks for NTL detection in a relatively smaller dataset containing occurrences of NTL. Experimental results demonstrate that deep learning based strategies have outperformed decision trees, random forest, and neural networks in terms of various performance metrics such as precision, recall, F1 and receiver operating characteristic (ROC) curve.…”

Section: Consumption-data Based Techniquesmentioning

confidence: 99%

Performance analysis of machine learning classifiers for non-technical loss detection

Ghori

Imran

Nawaz

et al. 2020

J Ambient Intell Human Comput

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Power companies are responsible for producing and transferring the required amount of electricity from grid stations to individual households. Many countries suffer huge losses in billions of dollars due to non-technical loss (NTL) in power supply companies. To deal with NTL, many machine learning classifiers have been employed in recent time. However, few has been studied about the performance evaluation metrics that are used in NTL detection to evaluate how good or bad the classifier is in predicting the non-technical loss. This paper first uses three classifiers: random forest, K-nearest neighbors and linear support vector machine to predict the occurrence of NTL in a real dataset of an electric supply company containing approximately 80,000 monthly consumption records. Then, it computes 14 performance evaluation metrics across the three classifiers and identify the key scientific relationships between them. These relationships provide insights into deciding which classifier can be more useful under given scenarios for NTL detection. This work can be proved to be a baseline not only for the NTL detection in power industry but also for the selection of appropriate performance evaluation metrics for NTL detection.

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Section: Consumption-data Based Techniquesmentioning

confidence: 99%

Performance analysis of machine learning classifiers for non-technical loss detection

Ghori

Imran

Nawaz

et al. 2020

J Ambient Intell Human Comput

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“…In the US, the total annual loss is 6 billion dollars while for India, it is 4.5 billion dollars [2]. Similarly, Brazil suffers 4.5 billion dollars annually due to NTL [3]. Pakistan's economy is also suffering from 0.89 billion dollars annually on account of The associate editor coordinating the review of this manuscript and approving it for publication was Francesco Piccialli.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

et al. 2020

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With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection.

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“…Deep learning techniques for electricity theft detection are studied in [18], where the authors present a comparison between different deep learning architectures such as convolutional neural networks (CNNs), long-short-term memory (LSTM) recurrent neural networks (RNNs), and stacked autoencoders. However, the performance of the detectors is investigated using synthetic data, which does not allow a reliable assessment of the detector's performance compared with shallow architectures.…”

Section: Introductionmentioning

confidence: 99%

Electricity Theft Detection in Power Grids with Deep Learning and Random Forests

Shuan

Han

Yao

et al. 2019

Journal of Electrical and Computer Engineering

159

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As one of the major factors of the nontechnical losses (NTLs) in distribution networks, the electricity theft causes significant harm to power grids, which influences power supply quality and reduces operating profits. In order to help utility companies solve the problems of inefficient electricity inspection and irregular power consumption, a novel hybrid convolutional neural network-random forest (CNN-RF) model for automatic electricity theft detection is presented in this paper. In this model, a convolutional neural network (CNN) firstly is designed to learn the features between different hours of the day and different days from massive and varying smart meter data by the operations of convolution and downsampling. In addition, a dropout layer is added to retard the risk of overfitting, and the backpropagation algorithm is applied to update network parameters in the training phase. And then, the random forest (RF) is trained based on the obtained features to detect whether the consumer steals electricity. To build the RF in the hybrid model, the grid search algorithm is adopted to determine optimal parameters. Finally, experiments are conducted based on real energy consumption data, and the results show that the proposed detection model outperforms other methods in terms of accuracy and efficiency.

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Identifying Nontechnical Power Loss via Spatial and Temporal Deep Learning

Cited by 72 publications

References 21 publications

Performance analysis of machine learning classifiers for non-technical loss detection

Performance analysis of machine learning classifiers for non-technical loss detection

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

Electricity Theft Detection in Power Grids with Deep Learning and Random Forests

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