English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The problem of electricity theft is exponentially increasing around the globe, which is harmful to the power sectors and consumers. The recent development in the advanced metering infrastructure brings opportunities for experts to identify the electricity thieves in the smart grid community. Many advancements are made in the area of the smart grid for Electricity Theft Detection (ETD), where the data collected from the smart meters is utilized. However, the problems of imbalanced distribution of data and inaccurate classification are not efficiently addressed. Therefore, to overcome the problems, machine learning and deep learning models are proposed for ETD. Initially, to refine the smart meters&#x2019; data, pre-processing methods are used. Then, the class imbalance problem is solved through Synthetic Minority Oversampling Tomek Links (ST-Links). It solves the classifier&#x2019;s biasness problem, which occurs due to imbalanced data. It achieves the benefits of both data oversampling and undersampling. Afterwards, an AlexNet and peephole long short-term memory network based feature extractor with an attention layer is developed to extract the relevant features from electricity consumption profiles that are most suitable to classify honest and theft consumers. After the extraction of suitable features, the classification of consumers is performed by an echo state neural network. Moreover, an evolutionary grey wolf optimization technique is utilized to tune the hyper-parameters of the proposed model. A paired t-test is also applied on the final classification results for a reliable assessment of the proposed model. The simulations are conducted on a realistic smart meters&#x2019; dataset of China to check the performance of the proposed model. In addition, different benchmark models are implemented to perform a comparative analysis. Different meaningful performance metrics are considered for the fair evaluation of the proposed model: Matthews Correlation Coefficient (MCC), F1-score, Area Under Curve (AUC), precision and recall. The simulation results depict that the proposed model obtains accuracy, recall, F1-score, AUC, PR-AUC, precision and MCC score of 96.3&#x0025;, 92.1&#x0025;, 92.0&#x0025;, 96.4&#x0025;, 97.3&#x0025;, 90.0&#x0025; and 84.0&#x0025;, respectively. It is worth mentioning that the application of the proposed solution is quite general. Therefore, it can be used by the power companies to overcome the power losses in the energy sector.

A PLSTM, AlexNet and ESNN Based Ensemble Learning Model for Detecting Electricity Theft in Smart Grids