DESIGN AND OPTIMIZATION OF ENERGY-EFFICIENT SMART GRIDS USING IOT AND MACHINE LEARNING TECHNIQUES

Abstract

The increasing penetration of renewable energy sources and the growing complexity of power systems have necessitated the development of intelligent and energy-efficient smart grid solutions. This paper presents an integrated framework for the design and optimization of energy-efficient smart grids using Internet of Things (IoT) and machine learning techniques. Real-time data acquired from IoT-enabled sensors are utilized for load and renewable generation forecasting, enabling predictive and adaptive energy management. Machine learning models are employed to capture nonlinear relationships between environmental conditions, consumption patterns, and energy generation. An intelligent control strategy incorporating battery energy storage, demand response, and dynamic pricing is implemented to minimize grid dependency and transmission losses. System performance is evaluated using efficiency metrics, forecasting accuracy, and control action analysis. The results demonstrate that the proposed framework significantly improves energy efficiency, enhances renewable energy utilization, and supports reliable and sustainable smart grid operation.