Analysis and Functioning of Smart Grid for Enhancing Energy Efficiency Using OptimizationTechniques with IoT

The implementation of smart grids has emerged as a promising solution to enhance energy efficiency and address the challenges posed by the growing energy demands and environmental concerns. In this context, the integration of Internet of Things (IoT) technology has enabled the deployment of advanced monitoring and control systems for efficient energy management. The smart grid system utilizes IoT-enabled sensors and devices to collect real-time data on energy consumption, production, and environmental factors. These sensors provide a wealth of information that can be used to analyze energy patterns, identify inefficiencies, and make informed decisions to improve energy efficiency. Bayesian optimization techniques, a probabilistic approach to optimization, are employed to model the complex relationships between various parameters in the smart grid system By employing Bayesian optimization, the system can adaptively learn and optimize energy consumption based on historical data and real-time feedback. This enables the smart grid to dynamically adjust energy distribution, prioritize renewable energy sources, and optimize power generation and consumption schedules. The integration of IoT and Bayesian optimization techniques in the smart grid system facilitates improved energy efficiency by reducing wastage, minimizing carbon emissions, and enhancing overall grid performance. The real-time data provided by IoT devices allows for precise monitoring of energy flows and enables proactive maintenance and fault detection, leading to increased grid reliability.