IOT-Based Automatic Power Factor Correction with Real-Time Carbon Footprint Estimation

A Low-cost IoT-enabled Automatic Power Factor Correction (APFC) system with realtime carbon footprint estimation is designed and implemented using a Raspberry Pi Pico W microcontroller. The system continuously monitors the power factor of an electrical load and automatically engages optimal capacitor banks to correct a lagging power factor, achieving a stable value of approximately 0.99. This correction reduces energy waste, minimizes electricity costs, and enhances grid stability. A key innovation of this project is the integration of real-time carbon footprint estimation, where the system calculates and displays the amount of CO emissions prevented due to the improved energy efficiency, promoting environmental awareness alongside economic benefits. All critical parameters, including power factor, active power, and estimated CO savings, are transmitted wirelessly to a ThingSpeak IoT dashboard, allowing remote real-time monitoring and analysis. Performance was validated through MATLAB Simulink simulations and hardware prototyping, confirming the system’s effectiveness. The completed ThingSpeak integration enables intuitive visualization of system performance, making this project a holistic, scalable, and sustainable solution for intelligent energy management.