Alexnet-Adaboost-ABC Based Hybrid Neural Network for Electricity Theft Detection in Smart Grids

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

doi:10.1007/978-3-030-79725-6_24

Cited by 7 publications

(8 citation statements)

References 27 publications

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“…Furthermore, our model's Recall score, which measures the ability to capture positive instances, is only marginally lower than the benchmark model, demonstrating that our approach maintains a high ability to detect positive instances while significantly improving other performance aspects. These compelling results underscore the novelty and superiority of our "AlexNet" model over the benchmark "AlexNet-Adaboost-ABC-Based Hybrid Neural Network" proposed in research paper [50]. The substantial performance improvements achieved by our approach validate its efficacy in addressing classification tasks on balanced datasets.…”

Section: Et Classifiermentioning

confidence: 54%

“…Comparing the performance results from both tables, we observe that our proposed "AlexNet" model outperforms the benchmark model from research paper [50] across all evaluation metrics. Notably, our model achieved higher Precision, Accuracy, and F1 score values, indicating better precision-recall balance and overall classification accuracy.…”

Section: Et Classifiermentioning

confidence: 86%

“…It is concluded that the proposed ETD approach is scalable. To validate the effectiveness of our proposed method, we conducted extensive experiments and compared its performance with a benchmark model presented in research paper [50]. On the other hand, Table 8 illustrates the performance results of our proposed "AlexNet" model on the same balanced dataset.…”

Section: Et Classifiermentioning

confidence: 99%

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A novel deep learning technique to detect electricity theft in smart grids using AlexNet

Khan,

Shahid,

Alam

et al. 2024

IET Renewable Power Gen

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Electricity theft (ET), which endangers public safety, interferes with the regular operation of grid infrastructure, and increases revenue losses, is a significant issue for power companies. To find ET, numerous machine learning, deep learning, and mathematically based algorithms have been published in the literature. However, these models do not yield the greatest results due to issues like the dimensionality curse, class imbalance, inappropriate hyper‐parameter tuning of machine learning, deep learning models etc. A hybrid DL model is presented for effectively detecting electricity thieves in smart grids while considering the abovementioned concerns. Pre‐processing techniques are first employed to clean up the data from the smart meters, and then the feature extraction technique, AlexNet is used to address the curse of dimensionality. An actual dataset of Chinese smart meters is used in simulations to assess the efficacy of the suggested approach. To conduct a comparative analysis, various benchmark models are implemented as well. This proposed model achieves accuracy, precision, recall, and F1‐score, up to 86%, 89%, 86%, and 84%, respectively.

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Section: Et Classifiermentioning

confidence: 54%

Section: Et Classifiermentioning

confidence: 86%

Section: Et Classifiermentioning

confidence: 99%

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A novel deep learning technique to detect electricity theft in smart grids using AlexNet

Khan,

Shahid,

Alam

et al. 2024

IET Renewable Power Gen

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“…With AlexNet, the curse of dimensionality issue has been handled, while adaptive boosting (AdaBoost) classified normal consumers and energy thieves. The tuning of hyper-parameters remain critical to achieving better prediction accuracy; hence a bee colony optimization algorithm has been used to tune the AdaBoost, and AlexNet 35 .…”

Section: Related Workmentioning

confidence: 99%

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Shaikh

Nazir

Khan

et al. 2022

Sci Rep

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Smart grids and smart homes are getting people’s attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with “day as covariates” remained better than the 1, 2, 3, and 4-week scenarios.

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“…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

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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.

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Alexnet-Adaboost-ABC Based Hybrid Neural Network for Electricity Theft Detection in Smart Grids

Cited by 7 publications

References 27 publications

A novel deep learning technique to detect electricity theft in smart grids using AlexNet

A novel deep learning technique to detect electricity theft in smart grids using AlexNet

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

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

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