Energy-Efficient Smart Grid Management Using IoT Sensors and Support Vector Regression

The ever-increasing need for sustainable energy management in smart grids has resulted in the fusion of advanced technologies such as the Internet of Things (IoT) and machine learning. This study puts forward a smart grid management system that is energy-efficient and uses IoT sensors for data collection and Support Vector Regression (SVR) to predict energy optimization. In the available setup, the grid is installed with IoT-based smart meters and environmental sensors that are used to continuously track parameters such as power consumption, the voltage of the grid, load variations, and environmental status. Through preprocessing, the gathered data is cleaned and the SVR model is trained. The SVR model is then used to predict future energy loads and is able to achieve accuracy of a high order. This forecasting capability through the dynamic load balancing, the reduction of energy waste, and the increase of grid reliability. Furthermore, the system has an intelligent mechanism of flexible control that responds to the SVR predictions, optimizing the energy distribution and reducing the stress of peak load. This work adds to the field by mixing real-time IoT sensing with the robustness of SVR modeling, so as to improve decision-making and operational efficiency in a smart grid context. The simulation outcomes have proven the marked improvement in energy usage prediction accuracy, cost savings, and grid stability which, thus, certify the efficiency of the new method in the area of modern energy management applications.