Towards Energy Efficient Smart Grids: Data Augmentation Through BiWGAN, Feature Extraction and Classification Using Hybrid 2DCNN and BiLSTM

Asif, Muhammad; Kabir, Benish; Pamir,; Ullah, Ashraf; Munawar, Shoaib; Javaid, Nadeem

doi:10.1007/978-3-030-79728-7_12

Cited by 3 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our scenario, the balanced data are synthesized by six theft variants to cope with the realistic theft data. Manipulating techniques used for the synthesis of the data are as follows [ 42 , 43 , 44 , 45 , 46 ]:

…”

Section: Proposed System Modelmentioning

confidence: 99%

Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing

Munawar

Javaid

Khan

et al. 2022

Sensors

View full text Add to dashboard Cite

In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples’ nature. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary. In order to cope with a Theft Case scenario, theft data is ascertained and synthesized randomly by using six theft data variants. Theft data variants are benign class appertaining data samples which are modified and manipulated to synthesize malicious samples. Furthermore, a K-means minority oversampling technique is used to tackle the class imbalance issue. In addition, to enhance the detection of the classifier, abstract features are engineered using a stochastic feature engineering mechanism. Moreover, to carry out affine training of the model, balanced data are inputted in order to mitigate class imbalance issues. An integrated hybrid model consisting of Bi-Directional Gated Recurrent Units and Bi-Directional Long-Term Short-Term Memory classifies the consumers, efficiently. Afterwards, robustness performance of the model is verified using an attack vector which is subjected to intervene in the model’s efficiency and integrity. However, the proposed model performs efficiently on such unseen attack vectors.

show abstract

…”

Section: Proposed System Modelmentioning

confidence: 99%

Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing

Munawar

Javaid

Khan

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

Machine intelligence aware electricity theft detection for smart metering applications

Munawar

Khan

Chaudhary

et al. 2023

Waves in Random and Complex Media

View full text Add to dashboard Cite

Data Augmentation Using BiWGAN, Feature Extraction and Classification by Hybrid 2DCNN and BiLSTM to Detect Non-Technical Losses in Smart Grids

et al. 2022

View full text Add to dashboard Cite

In this paper, we present a hybrid deep learning model that is based on a two-dimensional convolutional neural network (2D-CNN) and a bidirectional long short-term memory network (Bi-LSTM)to detect non-technical losses (NTLs) in smart meters. NTLs occur due to the fraudulent use of electricity. The global integration of smart meters has proven to be beneficial for the storage of historical electricity consumption (EC) data. The proposed methodology learns the deep insights from the historical EC data and informs power utilities about the presence of NTLs. However, the effective detection of NTLs faces the problem of class imbalance that occurs due to the rare availability of fraudulent electricity consumers. To solve this issue, an evolutionary bidirectional Wasserstein generative adversarial network (Bi-WGAN) is employed. Bi-WGAN synthesizes the most plausible fraudulent EC samples by integrating an auxiliary encoder module. Besides, the inevitable curse of high dimensional data reduces the generalization ability of classifiers. The proposed hybrid model efficiently handles the highly dynamic data by utilizing its potent feature extracting capabilities. The one-dimensional daily EC data is passed to Bi-LSTM model for capturing the non-malicious changes from consumers' profiles. Meanwhile, 2D-CNN takes 2D weekly EC data as input to extract the potential features by applying different convolutions and pooling operations. Extensive experiments are conducted on a realistic smart meters dataset to prove the effectiveness of the proposed model. The results show that the proposed model outperforms the state-of-the-art models by achieving area under the curve receiver operating characteristics of 0.97 and precision-recall area under the curve of 0.98, which make it suitable for real-world scenarios.

show abstract

Towards Energy Efficient Smart Grids: Data Augmentation Through BiWGAN, Feature Extraction and Classification Using Hybrid 2DCNN and BiLSTM

Cited by 3 publications

References 28 publications

Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing

Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing

Machine intelligence aware electricity theft detection for smart metering applications

Data Augmentation Using BiWGAN, Feature Extraction and Classification by Hybrid 2DCNN and BiLSTM to Detect Non-Technical Losses in Smart Grids

Contact Info

Product

Resources

About