MONITORING

This study aimed to design and implement a low-cost microcontroller-based system for monitoring the energy consumption of individual workshop machines, addressing the limitations of conventional centralized metering systems that fail to provide machine- specific data. The literature review examined previous work on energy monitoring technologies, including commercial, open-source, and academic systems, highlighting the growing role of the Internet of Things (IoT) in enabling real-time data acquisition and remote monitoring. It emphasized the need for affordable, scalable, and educationally adaptable solutions for developing regions, where technical expertise and financial resources are limited. The research adopted an experimental design methodology involving hardware and software integration. The system was built using Arduino Nano and ESP32 microcontrollers, ZMPT101B voltage and SCT-013 current sensors, an LCD display, and a ThingSpeak IoT cloud interface. Mathematical modeling was applied to compute voltage, current, power, energy, and cost, while SolidWorks was used for casing design. Calibration and testing were conducted under varying load conditions to assess accuracy, response time, and data stability. Data were logged both locally on an SD card and remotely on the cloud for redundancy and analysis. Results indicated that the system achieved high accuracy within ±1% for voltage and ±5% for current, with an overall efficiency of 95% and IoT data transfer uptime of 98%. The developed prototype successfully provided real-time monitoring, stable performance, and reliable data transmission. The study concluded that the Arduino-based energy monitoring system is a cost-effective, scalable, and efficient solution suitable for educational, domestic,v and small-scale industrial applications. It recommended future enhancements in predictive analytics, multi-machine scalability, and integration with renewable energy management platforms.