Smart power management system for an IOT device( case study selected is based on a raspberry PI)

Abstract

The increasing demand for energy-efficient and automated systems has prompted the development of smart power management solutions. In this study, we propose a smart power management system based on a Raspberry Pi, integrated with an Arduino, RTC module, relay, DHT sensor, coulomb sensor and Blynk application. The system is designed to automatically turn the Raspberry Pi on for 20 minutes each hour, and completely keep it off during the night periods effectively reducing power consumption during idle periods while ensuring availability when required. The Arduino acts as the intermediary between the Raspberry Pi and the peripheral components, facilitating communication and control. The real-time clock (RTC) module ensures accurate timekeeping and scheduling of the power cycles. A relay is employed to control the power supply to the Raspberry Pi, allowing for seamless switching between the on and off states. To monitor the power management system, we have integrated a DHT sensor that measures temperature and humidity levels within the Raspberry Pi enclosure and coulomb sensor. This information is relayed to the Blynk app, providing real-time environmental data to the user. The Blynk app offers a user-friendly interface to remotely control the power state of the Raspberry Pi, enabling manual intervention if needed. The proposed system offers an efficient and user-friendly solution for power management in Raspberry Pi-based applications. By intelligently controlling the power cycles and providing environmental monitoring, it optimizes energy consumption while maintaining system availability. Future recommendations may include integrating additional sensors for broader environmental monitoring and incorporating machine learning algorithms for predictive power management strategies. The project outcomes will contribute to the advancement of smart power management in IoT systems, promoting energy efficiency and sustainability. The results and insights gained from this project can be utilized by researchers, developers, and IoT enthusiasts in creating energy-conscious IoT solutions.