IOT-BASED SMART GRIDS: ADAPTIVE ENERGY SCHEDULING FRAMEWORK

Advances in global energy demand require the establishment of smart and efficient energy management systems. This paper advances an IoT-based smart grid that dynamically optimizes load distribution according to peak hour timing, ambient light conditions, and real-time energy consumption. The system is integrated with an ESP32 microcontroller, DS3231 RTC module, LDR sensor, and PEM-004T energy meter to monitor energy parameters and execute real-time adjustments. The primary aim of this system is to decrease peak demand, enhance energy saving, and provide a sustainable and efficient power grid. Remote monitoring and control capabilities allow for real-time decision-making in the management of loads. The system has been tested in real-time implementation, with impressive improvements in load balancing and energy efficiency. Experimental results verify the capability of the proposed model to minimize energy wastage and optimize power consumption by using IoT-enabled real-time data analysis. The system is shown to exhibit a 15–20% improvement in energy efficiency, pointing out the urgent need for smart energy solutions in modern power grids.