IoT enabled implementation of a smart energy management system for real-time monitoring and controlling
DOI:
https://doi.org/10.21015/vtcs.v12i2.1952Abstract
The Pakistan power system faces a severe energy deficiency due to population growth. The Pakistan power system structure necessitates a smart load management system to address the mismanagement of load distribution that results in load shedding. A continuous power supply is essential for industrial and domestic sectors to enhance Pakistan's economy. Imported fuel presents a significant economic challenge in Pakistan. This system is designed to control and monitor load management utilizing Internet of Things (IoT)-based technology. The implementation of an IoT-based approach enhances system performance by upgrading conventional load management. This study analyzed effective energy management during peak hours. This system employs a smart energy management system to improve load shedding patterns. The system components include a power monitoring module, current sensor, transformer, Wi-Fi module, and relay module integrated with an Arduino-based microcontroller for monitoring and controlling load management. To model a virtual analysis a simulation based circuit diagram is design using Proteus Software. The implementation of this study improves energy transactions between utilities and consumers. In this study, a prototype was designed and implemented using Arduino, and sensors were employed to control and observe a smart load management system. This system utilizes a hypertext preprocessor for login webpage arrangement for IoT control and monitoring. The results indicate that the cumulative analysis for the implementation of this system automatically deactivates unnecessary loads during peak hours. This system can conserve energy during peak-hour intervals. The load parameters for current, voltage, and power were displayed on the LCD screen. The system automatically deactivates the load during harmonics in the power supply until normal conditions are restored. As compared to existing studies this system detects and switch the system during on and off peak hours without any human interaction.
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