Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid

Ayub, Nasir; Ali, Usman; Mustafa, Kainat; Mohsin, Syed Muhammad; Aslam, Sheraz

doi:10.3390/forecast4040051

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…A proposed security system in study [12] addresses secure communication in IoT-driven smart cities using a detection concept. Utilizing neural network-based training, the system track local and global changes in the sharing of data among IoT devices in order to detect vulnerabilities in resource access and bolster overall security.…”

Section: Related Workmentioning

confidence: 99%

Inclusive Smart Cities: IoT-Cloud Solutions for Enhanced Energy Analytics and Safety

Almazroi,

Alsubaei,

Ayub

et al. 2024

ijacsa

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Securing smart cities in the evolving Internet of Things (IoT) demands innovative security solutions that extend beyond conventional theft detection. This study introduces temporal convolutional networks and gated recurrent units (TCGR), a pioneering model tailored for the dynamic IoT-SM dataset, addressing eight distinct forms of theft. In contrast to conventional techniques, TCGR utilizes Jaya tuning (TCGRJ), ensuring improved accuracy and computational efficiency. The technique employs ResNeXt for feature extraction to extract important patterns from IoT device-generated data and Edited Nearest Neighbors for data balancing. Empirical evaluations validate TCGRJ's greater precision (96.7%) and accuracy (97.1%) in detecting theft. The model significantly aids in preventing theftrelated risks and is designed for real-time Internet of Things applications in smart cities, aligning with the broader goal of creating safer spaces by reducing hazards associated with unauthorized electrical connections. TCGRJ promotes sustainable energy practices that benefit every resident, particularly those with disabilities, by discouraging theft and encouraging economical power consumption. This research underscores the crucial role of advanced theft detection technologies in developing smart cities that prioritize inclusivity, accessibility, and an enhanced quality of life for all individuals, including those with disabilities.

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Section: Related Workmentioning

confidence: 99%

Inclusive Smart Cities: IoT-Cloud Solutions for Enhanced Energy Analytics and Safety

Almazroi,

Alsubaei,

Ayub

et al. 2024

ijacsa

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“…In a more recent study, Nasir Ayub et al (2022) have proposed a new approach to the issue, with the study "Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid". In this ambitious research, the authors developed a fraud detection model that merges a Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU).…”

Section: Literature Reviewmentioning

confidence: 99%

Electrical Network Fraud Detection Using a CNN+LSTM Model

A. Gomez,

A. Llaugel

2023

IJAEMR

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This work focuses on the persistent problem of energy loss in the Dominican Republic and in Latin America, with an emphasis on non-technical losses caused by electricity fraud. Given the seriousness of the problem, this paper proposes the development and implementation of a fraud classification model in electricity distribution networks using Deep Learning, specifically a combined model of a Convolutional Neural Network Distributed in Time (CNN) and Long Short Term Memory (LSTM). The goal is to understand and evaluate current fraud detection techniques, investigate the applicability and efficiency of CNN + LSTM models in fraud detection, and address potential challenges in implementing this model in Latin America. The justification lies in the considerable financial losses generated by electricity fraud.

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“…The article [37] delved into the combination of power cost estimation and energy demand prediction, utilizing the Artificial Bee Colony and SVM algorithms with Least Square. On the other hand, [38] proposed an ANN-based approach, and [39] put forward a hybrid methodology employing a model based on a biweight kernel with dynamical system reconstruction to forecast electricity prices using datasets from the ISO of New York, the US, and the South Wales markets. However, these models are computationally expensive, generate inaccurate predictions resulting in significant losses, and are inefficient for real-time use.…”

Section: Related Workmentioning

confidence: 99%

Multi-Task Learning for Electricity Price Forecasting and Resource Management in Cloud Based Industrial IoT Systems

Almazroi

Ayub

2023

IEEE Access

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Cloud computing has gained immense popularity in the logistics industry. This innovative technology optimizes computing operations by eliminating the requirement for physical equipment for calculations. Instead, specialized companies provide cloud-based computing services, relying heavily on computers and servers that consume substantial amounts of energy. Hence, ensuring the availability of affordable and dependable electricity is paramount for the efficient design and management of these logistics services. Cloud centers, which are power-intensive, face the challenge of reducing their energy consumption due to escalating power costs. To address this issue, efficient data placement and node management strategies are commonly employed in logistics operations. An AlexNet model has been designed to optimize storage relocation and predict power prices. The outcome of this initiative has resulted in a considerable reduction in energy consumption at data centres. The model uses Dwarf Mongoose Optimization Algorithm (DMOA) to produce an optimal solution for the AlexNet and increase its performance with a real-world dataset from IESO in Ontario, Canada. 75% of the available data was used for training to assure the model's precision, with the remaining 25% allocated to testing purposes. The model forecasts power prices with an MAE of 2.22% and an MSE of 6.33%, resulting in an average reduction of 22.21% in electricity expenses. Our proposed method has an accuracy of 97% compared to 11 benchmark algorithms, including CNN, DenseNet, and SVM having an accuracy of 89%, 88%, and 82%, respectively. INDEX TERMSCloud systems, deep learning, energy efficiency, energy consumption, machine learning, Meta heuristic algorithm, price forecasting, logistics. NASIR AYUB (Student Member, IEEE) received the M.S. degree from COMSATS University Islamabad, Pakistan, in the domain of smart grid and data science. He is currently pursuing the Ph.D. degree in computer science (specialization in blockchain) with the School of Electrical Engineering and Computer Science,

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Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid

Cited by 6 publications

References 28 publications

Inclusive Smart Cities: IoT-Cloud Solutions for Enhanced Energy Analytics and Safety

Inclusive Smart Cities: IoT-Cloud Solutions for Enhanced Energy Analytics and Safety

Electrical Network Fraud Detection Using a CNN+LSTM Model

Multi-Task Learning for Electricity Price Forecasting and Resource Management in Cloud Based Industrial IoT Systems

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