IOT-Driven Smart Energy Grid With Predictive Load Management Using Raspberry Pi Pico

Abstract

The evolution of smart energy grids has transitioned from conventional power distribution to intelligent, IoT-driven systems capable of real-time monitoring and predictive analytics. Traditional energy management relied on static load distribution, often leading to inefficiencies, energy wastage, and grid instability. With the integration of IoT, smart grids can now leverage sensor-driven data acquisition to monitor key parameters such as temperature, voltage, current, and grid frequency. Utilizing the Raspberry Pi Pico, this system employs machine learning techniques to analyse consumption patterns and optimize energy distribution. The incorporation of IoT enables remote monitoring and control, enhancing operational efficiency and sustainability. By implementing intelligent load management strategies, the proposed system contributes to grid stability, energy conservation, and the broader adoption of smart energy solutions. The integration of predictive analytics further ensures that future load requirements are anticipated, preventing failures and improving energy efficiency across large-scale grids. Additionally, the use of edge computing in conjunction with cloud storage enhances the system’s responsiveness, reducing delays and enabling more dynamic energy allocation.