Recharging Prepaid Energy Meter via Internet Using ESP32-CAM and Telegram Messenger

Yohana Susanthi; Herawati YS; Judea Janoto Jarden; Ken Fernando Tentunata

doi:10.22146/jnteti.v15i3.22444

Yohana Susanthi Bachelor Program in Electrical Engineering, Faculty of Smart Technology and Engineering, Universitas Kristen Maranatha, Bandung, Jawa Barat 40164, Indonesia
Herawati YS Bachelor Program in Electrical Engineering, Faculty of Smart Technology and Engineering, Universitas Kristen Maranatha, Bandung, Jawa Barat 40164, Indonesia
Judea Janoto Jarden Bachelor Program in Electrical Engineering, Faculty of Smart Technology and Engineering, Universitas Kristen Maranatha, Bandung, Jawa Barat 40164, Indonesia
Ken Fernando Tentunata Bachelor Program in Electrical Engineering, Faculty of Smart Technology and Engineering, Universitas Kristen Maranatha, Bandung, Jawa Barat 40164, Indonesia

DOI: https://doi.org/10.22146/jnteti.v15i3.22444

Keywords: Prepaid Energy Meter, Electricity Token, ESP32-CAM, Telegram Messenger, Sound Recognition

Abstract

In Indonesia, the requirement to manually enter electricity tokens into prepaid energy meters poses a significant challenge, particularly when customers are away from home. The inability to recharge remotely poses a risk of power outages and potential material loss. This research aimed to design an Internet-based remote recharging system that automated the physical button-pressing process and monitored the meter’s status remotely. Additionally, the system should be able to detect alarm sounds from the meter and send notifications to the customer when the electricity units are running low. The system consisted of a Scotch yoke mechanism with three servo motors controlled by an ESP32-CAM to move the arm and press the button on the prepaid energy meter. A sound rhythm-based pattern recognition method was used to detect alarm sounds with NodeMCU. Test results indicated a 100% success rate in recharging the prepaid energy meter. The average total time taken for the system to recharge the prepaid energy meter was 65.69 s. The highest accuracy score, 94.5%, was achieved by the system in detecting alarm sounds. The maximum noise level for the system to effectively detect alarm sounds was 55 dB. The implementation of this internet-based system eliminates physical constraints in recharging prepaid energy meters, thereby enhancing user accessibility and convenience.

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