An Energy-Fraud Detection-System Capable of Distinguishing Frauds from Other Energy Flow Anomalies in an Urban Environment

Calamaro, Netzah; Beck, Yuval; Melech, Ran Ben; Shmilovitz, Doron

doi:10.3390/su131910696

Cited by 7 publications

(4 citation statements)

References 40 publications

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“…Energies 2021, 14, x FOR PEER REVIEW 33 of 39 The proposed procedure follows Equation (24). What is suggested herein is to perform the disaggregation algorithm at the spectral space over the FFT of energy or other sampled electric parameters, where the periodicity should be daily for a quarter hourly load profile and about four seconds for P1 dsmr time-series voltage and current data.…”

Section: A Discussion On Future Research Implied By the Presented Researchmentioning

confidence: 99%

“…The larger the dimensional count, provided that it generates new information, the further away the separate device signature may be located. An example work using collaborative entire load-profile data features with another clustering-type grid analytics algorithm for fraud/non-fraud is [24]. Article [25] by Majumdar et al also explicitly discusses that all/most NILM algorithms handle all the on/off scenarios of a collaborative cluster of devices.…”

Section: Basic Definitions For Algorithm Presentationmentioning

confidence: 99%

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A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements

Calamaro¹,

Donko²,

Shmilovitz³

2021

Energies

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The central problems of some of the existing Non-Intrusive Load Monitoring (NILM) algorithms are indicated as: (1) higher required electrical device identification accuracy; (2) the fact that they enable training over a larger device count; and (3) their ability to be trained faster, limiting them from usage in industrial premises and external grids due to their sensitivity to various device types found in residential premises. The algorithm accuracy is higher compared to previous work and is capable of training over at least thirteen electrical devices collaboratively, a number that could be much higher if such a dataset is generated. The algorithm trains the data around 1.8×108 faster due to a higher sampling rate. These improvements potentially enable the algorithm to be suitable for future “grids and industrial premises load identification” systems. The algorithm builds on new principles: an electro-spectral features preprocessor, a faster waveform sampling sensor, a shorter required duration for the recorded data set, and the use of current waveforms vs. energy load profile, as was the case in previous NILM algorithms. Since the algorithm is intended for operation in any industrial premises or grid location, fast training is required. Known classification algorithms are comparatively trained using the proposed preprocessor over residential datasets, and in addition, the algorithm is compared to five known low-sampling NILM rate algorithms. The proposed spectral algorithm achieved 98% accuracy in terms of device identification over two international datasets, which is higher than the usual success of NILM algorithms.

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Section: A Discussion On Future Research Implied By the Presented Researchmentioning

confidence: 99%

Section: Basic Definitions For Algorithm Presentationmentioning

confidence: 99%

A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements

Calamaro¹,

Donko²,

Shmilovitz³

2021

Energies

Self Cite

View full text Add to dashboard Cite

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“…From the literature review, in [90], a multi-agent-based simulation of the distribution networks of a city in Germany reached the objective of the article of successfully simulating the dynamic model of the city energy network, and this simulation was compared to professional simulation tools, which had a relative error lower than 0.0000084%. Another important approach to the application of AI in energy distribution is present in [80], which explains the development of an algorithm to solve energy fraud detection to minimize energy loss in the electricity grid. This algorithm used Convolutional Neural Networks (CNNs) and Robotic Process Automation (RPA) to detect precise fraud identification in electricity networks, the main objective of which was to separate electricity fraud from the many other anomalies that could be presented in the network.…”

Section: Energy Distributionmentioning

confidence: 99%

Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review

Camacho,

Aguirre,

Ponce

et al. 2024

Energies

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As Smart Cities development grows, deploying advanced technologies, such as the Internet of Things (IoT), Cyber–Physical Systems, and particularly, Artificial Intelligence (AI), becomes imperative for efficiently managing energy resources. These technologies serve to coalesce elements of the energy life cycle. By integrating smart infrastructures, including renewable energy, electric vehicles, and smart grids, AI emerges as a keystone, improving various urban processes. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and the Scopus database, this study meticulously reviews the existing literature, focusing on AI technologies in four principal energy domains: generation, transmission, distribution, and consumption. Additionally, this paper shows the technological gaps when AI is implemented in Smart Cities. A total of 122 peer-reviewed articles are analyzed, and the findings indicate that AI technologies have led to remarkable advancements in each domain. For example, AI algorithms have been employed in energy generation to optimize resource allocation and predictive maintenance, especially in renewable energy. The role of AI in anomaly detection and grid stabilization is significant in transmission and distribution. Therefore, the review outlines trends, high-impact articles, and emerging keyword clusters, offering a comprehensive analytical lens through which the multifaceted applications of AI in Smart City energy sectors can be evaluated. The objective is to provide an extensive analytical framework that outlines the AI techniques currently deployed and elucidates their connected implications for sustainable development in urban energy. This synthesis is aimed at policymakers, urban planners, and researchers interested in leveraging the transformative potential of AI to advance the sustainability and efficiency of Smart City initiatives in the energy sector.

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“…At the same time, improving the efficiency of electricity inspection can also improve service quality and enhance user experience. Many power firms have pursued ways of improving the sustainability of intelligent electricity inspection and reducing economic losses, such as by modifying lines, introducing high-tech power-stealing devices, and changing the performance of electric energy meters [1,2]. In addition, the costs of installing inspection equipment in some areas where electricity consumption is not concentrated, as well as the low efficiency of human inspection and waste of resources, have contributed to the challenges of electricity inspection [3].…”

Section: Introductionmentioning

confidence: 99%

Improving the Efficiency and Sustainability of Intelligent Electricity Inspection: IMFO-ELM Algorithm for Load Forecasting

Tian¹,

Zou²,

Wang³

et al. 2022

Sustainability

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Electricity inspection is important to support sustainable development and is core to the marketing of electric power. In addition, it contributes to the effective management of power companies and to their financial performance. Continuous improvement in the penetration rate of new energy generation can improve environmental standards and promote sustainable development, but creates challenges for electricity inspection. Traditional electricity inspection methods are time-consuming and quite inefficient, which hinders the sustainable development of power firms. In this paper, a load-forecasting model based on an improved moth-flame-algorithm-optimized extreme learning machine (IMFO-ELM) is proposed for use in electricity inspection. A chaotic map and improved linear decreasing weight are introduced to improve the convergence ability of the traditional moth-flame algorithm to obtain optimal parameters for the ELM. Abnormal data points are screened out to determine the causes of abnormal occurrences by analyzing the model prediction results and the user’s actual power consumption. The results show that, compared with existing PSO-ELM and MFO-ELM models, the root mean square error of the proposed model is reduced by at least 1.92% under the same conditions, which supports application of the IMFO-ELM model in electricity inspection. The proposed power-load-forecasting-based abnormal data detection method can improve the efficiency of electricity inspection, enhance user experience, contribute to the intelligence level of power firms and promote their sustainable development.

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An Energy-Fraud Detection-System Capable of Distinguishing Frauds from Other Energy Flow Anomalies in an Urban Environment

Cited by 7 publications

References 40 publications

A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements

A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements

Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review

Improving the Efficiency and Sustainability of Intelligent Electricity Inspection: IMFO-ELM Algorithm for Load Forecasting

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