Electricity theft is one of the major issues in developing countries which is affecting their economy badly. Especially with the introduction of emerging technologies, this issue became more complicated. Though many new energy theft detection (ETD) techniques have been proposed by utilising different data mining (DM) techniques, state & network (S&N) based techniques, and game theory (GT) techniques. Here, a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations. Three levels of taxonomy are presented to classify state-of-the-art ETD techniques. Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature. The challenges of different ETD techniques and their mitigation are suggested for future work. It is observed that the literature on ETD lacks knowledge management techniques that can be more effective, not only for ETD but also for theft tracking. This can help in the prevention of energy theft, in the future, as well as for ETD. 

Energy Theft Detection in Smart Grids: Taxonomy, Comparative Analysis, Challenges, and Future Research Directions

Electricity theft has caused enormous damage to grids safety and economy globally, bringing plentiful attention to electricity theft detection. However, the inherent problems of data imbalance, data sparsity, and data shift due to the residents will or the seasons remain challenges. Besides, there is a lack of appliance-level information provided by non-intrusive load monitoring to improve the data graininess in electricity theft detection. To address these problems, an electricity theft detection algorithm based on contrastive learning and non-intrusive load monitoring is proposed. Firstly, a semi-supervised learning architecture composed of Gramian angular field encoding for sequence visualization and contrastive learning architecture featuring few-shot learning is established for load monitoring and initial detection considering the inherent operating characteristics of appliances. Furthermore, after the filtration of typical regular-switched appliances by Kendalls coefficient of concordance, in-depth detection of abnormal operation routines of appliances is conducted for suspicious residents, aiming to improve the fault-tolerant ability and resist the emergence of unknown electricity theft methods. Finally, the electricity theft probability is computed to confirm fraudulent users. Both the public and practical datasets are utilized to verify the effectiveness of the proposed study, and the results show an overall better performance compared to other state-of-the-art algorithms. 

Electricity theft detection based on contrastive learning and non-intrusive load monitoring

Modelling and simulation of dual heating of substrate with centralized temperature control for anaerobic digestion process

Electricity theft has been a growing concern for the smart grid. It can be defined as follows: illegal customers use energy from electric utilities without a contract or manipulate their meter readings to pay less or not pay the electricity bill. Over the past decade, significant studies have been done to prevent and combat theft. This article aims to provide a general overview of the progress of electricity theft detection, including threat models, datasets and input features adopted, methodologies and techniques, and evaluation metrics. We also make a performance comparison for each detection method. Finally, the challenges and future research directions are presented in this article. 

Performance Analysis of Electricity Theft Detection for the Smart Grid: An Overview

The use of prepaid smart electricity services from the State Electricity Company (SEC) for the general public in Indonesia has increased. It is because prepaid electricity has several advantages compared to postpaid electricity. One of them is that it is becoming easier for customers to manage and control their daily electricity usage. Customers can also estimate their total electricity consumption each month. However, customers still have to manually view the information on the electricity meter to find data and information on prepaid electricity. It can make it difficult for customers if the electricity meter is placed outside the house, which is quite far away. Customers must also constantly monitor the use of electricity. This article discusses a prepaid smart electricity consumption monitoring design using Internet of Things technology. The design is carried out without changing the standard prepaid electricity meter system from the SEC. However, a KWH meter tool has been developed that has been combined with Internet of Things technology and can calculate and monitor electricity usage. The results show that with this system, customers can more easily find out and monitor their daily electricity consumption. 
 

/pdf/pre-semms-a-design-of-prepaid-smart-energy-meter-monitoring-21wqvd0u.pdf

Pre-SEMMS: A Design of Prepaid Smart Energy Meter Monitoring System for Household Uses Based on Internet of Things

This paper proposes a mathematical model of multi-featured prepaid-postpaid energy meter. These features are (a) automatic billing, (b) prepaid collection, (c) choice of prepaid or postpaid, (d) detection of abnormal operational condition, (e) protection against electricity theft, (f) data sharing between consumer and utility end, (g) improved accuracy, (h) user friendly as well as economical and (i) motivating towards electric energy conservation. The model uses RS-232 and GSM module for two-way communication. The proposed model is simulated and tested over MATLAB/ Simulink®platform. The results obtained are promising and complying with the existing features of the energy meter.

Design and Simulation of Smart Prepaid-Postpaid Energy Meter with Alarm and Theft Control

This paper presents an off-grid hybrid power system. This hybrid power is sustained by three power sources, i.e., sunlight based power by photovoltaics, battery bank, and bio-gas. To meet a relatively downsized electricity demand in comparison to main grid load, i.e., the load of small villages, household appliances or small domestic industries of the order of 1.6 MW, a satisfactory amount of generation can be available using the hybrid bio-gas plant, results in meeting the peak demand and getting the garbage used. The results of the simulation evinces that the amplitudes and periods of AC yield voltage are very much controlled. It is estimated that this type of power generation system, in which renewable energies are being utilized, will supply electric power and give security of the earth in rustic areas and rural zone with no reliance on grid control supply.

Standalone Hybrid Power Generation using Photovoltaics, Battery and Bio-gas

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Design and Simulation of Smart Prepaid-Postpaid Energy Meter with Alarm and Theft Control

Standalone Hybrid Power Generation using Photovoltaics, Battery and Bio-gas