IOT-BASED SMART ENERGY METER MONITORING WITH THEFT CONTROL

Electricity theft is an escalating issue, worsened by global warming, which contributes to unbalanced power supply and increased safety concerns. Unauthorized power usage exceeding supply limits often leads to system shutdowns and reduced transmission efficiency. Illegal circuit bypassing has also caused fires, resulting in property damage and threats to public safety. This study presents the design and development of a real-time Internet of Things (IoT)-based smart meter using Arduino technology to monitor energy usage and detect electricity theft. The proposed system involves partial circuit simulation and the development of a full prototype using a direct current (DC) circuit. A Global System for Mobile Communications (GSM) module is integrated to provide remote monitoring and control through Short Message Service (SMS), offering reliable and cost-effective connectivity. When theft is detected, the system triggers a buzzer alarm, displays warnings, and sends power shutdown alerts via GSM. Authorities can then intervene using SMS commands to restore safety. Energy consumption is monitored in real time with updates every 30 seconds. The prototype was tested and successfully validated, showing a maximum error margin of 18% when comparing real and measured power data. The system demonstrated efficient theft detection, real-time monitoring, and remote power control capabilities. This study confirms the feasibility of an Arduino-GSM-based IoT smart meter for real-time electricity monitoring and theft detection. The proposed solution enhances grid safety and efficiency by providing accurate energy tracking, early warning systems, and remote intervention tools.

Authors

Publication Details

Journal
Journal of Engineering & Technological Advances
Published
2025-04-25
DOI
https://doi.org/10.35934/segi.v9i2.122
Citations
1
Primary Topic
Electricity Theft Detection Techniques
Type
article
Field-Weighted Citation Impact
0.73
Controls
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article

IOT-BASED SMART ENERGY METER MONITORING WITH THEFT CONTROL

Hock Siong Chong
1 citations
Journal of Engineering & Technological Advances
Electricity Theft Detection Techniques
0.73
article

IOT-BASED SMART ENERGY METER MONITORING WITH THEFT CONTROL

Hock Siong Chong
article en
1 citations

Abstract

Electricity theft is an escalating issue, worsened by global warming, which contributes to unbalanced power supply and increased safety concerns. Unauthorized power usage exceeding supply limits often leads to system shutdowns and reduced transmission efficiency. Illegal circuit bypassing has also caused fires, resulting in property damage and threats to public safety. This study presents the design and development of a real-time Internet of Things (IoT)-based smart meter using Arduino technology to monitor energy usage and detect electricity theft. The proposed system involves partial circuit simulation and the development of a full prototype using a direct current (DC) circuit. A Global System for Mobile Communications (GSM) module is integrated to provide remote monitoring and control through Short Message Service (SMS), offering reliable and cost-effective connectivity. When theft is detected, the system triggers a buzzer alarm, displays warnings, and sends power shutdown alerts via GSM. Authorities can then intervene using SMS commands to restore safety. Energy consumption is monitored in real time with updates every 30 seconds. The prototype was tested and successfully validated, showing a maximum error margin of 18% when comparing real and measured power data. The system demonstrated efficient theft detection, real-time monitoring, and remote power control capabilities. This study confirms the feasibility of an Arduino-GSM-based IoT smart meter for real-time electricity monitoring and theft detection. The proposed solution enhances grid safety and efficiency by providing accurate energy tracking, early warning systems, and remote intervention tools.

Journal of Engineering & Technological AdvancesVol. 9(2)
Openalex Percentile: Top 27%
Electricity Theft Detection Techniques
0.73
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