Towards Efficient Energy Utilization Using Big Data Analytics in Smart Cities for Electricity Theft Detection

Arif, Arooj; Alghamdi, Turki Ali; Khan, Zahoor Ali; Javaid, Nadeem

doi:10.1016/j.bdr.2021.100285

Cited by 44 publications

(16 citation statements)

References 29 publications

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“…These efficiencies can be applied to monitoring and controlling energy networks through several thousand sensors. The articles classified in this dimension comprise applications such as automatic detection of electricity theft (Arif et al., 2022), smart grid operation (Nandury & Begum, 2018), energy management of buildings (Mohamed et al., 2018), electricity price and load forecasting (Mujeeb et al., 2019), and the analysis of behavioral patterns of electricity consumption (Pérez‐Chacón et al., 2018).…”

Section: Resultsmentioning

confidence: 99%

Research trends in the application of big data in smart cities—A literature review

2023

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In this study, we review articles published in academic journals. in the last 5 years at the intersection of big data and the smart city context. Based on a dataset of 192 articles, we use content analysis to identify recent trends and future research directions. The most influential research areas identified include using big data analytics, electric vehicle integration, and citizen‐centric big data, while the most recent research area that emerged is green space. Our results highlight how big data can influence eight different dimensions of smart cities, with smart mobility attracting the most attention from researchers in the application of big data in smart cities. In contrast, smart people and smart economy are the least explored dimensions from this perspective.

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Section: Resultsmentioning

confidence: 99%

Research trends in the application of big data in smart cities—A literature review

2023

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“…Moreover, deep learning models suffer from high variance in their final results due to the assignment of different weights. Therefore, an averaging ensemble strategy is applied in this work to reduce the high variance (Arif et al, 2022).…”

Section: Review Of Existing Literaturementioning

confidence: 99%

Electricity theft detection in smart grid using machine learning

Iftikhar,

Khan,

Raza

et al. 2024

Front. Energy Res.

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Nowadays, electricity theft is a major issue in many countries and poses a significant financial loss for global power utilities. Conventional Electricity Theft Detection (ETD) models face challenges such as the curse of dimensionality and highly imbalanced electricity consumption data distribution. To overcome these problems, a hybrid system Multi-Layer Perceptron (MLP) approach with Gated Recurrent Units (GRU) is proposed in this work. The proposed hybrid system is applied to analyze and solve electricity theft using data from the Chinese National Grid Corporation (CNGC). In the proposed hybrid system, first, preprocess the data; second, balance the data using the k-means Synthetic Minority Oversampling Technique (SMOTE) technique; third, apply the GTU model to the extracted purified data; fourth, apply the MLP model to the extracted purified data; and finally, evaluate the performance of the proposed system using different performance measures such as graphical analysis and a statistical test. To verify the consistency of our proposed hybrid system, we use three different ratios for training and testing the dataset. The outcomes show that the proposed hybrid system for ETD is highly accurate and efficient compared to the other models like Alexnet, GRU, Bidirectional Gated Recurrent Unit (BGRU) and Recurrent Neural Network (RNN).

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“…In order to build smart cities, many countries have introduced big data analysis and mining technology into urban construction and development and demonstrated feasibility, generality and effectiveness of Urban Data Science (Balduini et al, 2019). Big data analysis and mining technology have wide applications for urban transportation (Bachechi et al, 2022), energy efficiency (Arif et al, 2022), health protection (Imran et al, 2021) and so on. Therefore, as global hubs, the basic research of big data analysis and mining technology has been placed in an unprecedented position (Balduini et al, 2019).…”

Section: Big Data Analysis and Mining Technologymentioning

confidence: 99%

The Network Structures and Characteristics of Global Hubs of STI in Big Data Analysis and Mining Technology

Cao

et al. 2023

Science, Technology and Society

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Global hubs of STI are strategic nodes in the global science network and will lead the future of global scientific progress. This paper takes big data analysis and mining technology as an example, exploring the structures and characteristics of global science network of 15 representative global hubs of STI from 2000 to 2019. Based on the data of highly cited papers and methods of social network analysis, we find that the global science network has the structure of hierarchy, with an obvious characteristic of regionality. Based on the structures and characteristics of global science network, the knowledge flow and management model of global hubs has been proposed, which may be conducive to strengthening the knowledge management and network collaboration among global hubs of STI in other frontier technologies.

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Towards Efficient Energy Utilization Using Big Data Analytics in Smart Cities for Electricity Theft Detection

Cited by 44 publications

References 29 publications

Research trends in the application of big data in smart cities—A literature review

Research trends in the application of big data in smart cities—A literature review

Electricity theft detection in smart grid using machine learning

The Network Structures and Characteristics of Global Hubs of STI in Big Data Analysis and Mining Technology

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