Smart Energy Optimization Harnessing IoT and Advanced Learning Techniques

The paper gives an extensive investigation of the research findings on the optimization of energy management in smart microgrids. The study specifically focuses on integrating renewable energy sources with the Internet of Things. Smart microgrids have become an essential option due to the increasing global energy demand and environmental problems arising from the use of fossil fuels. These grids assure better efficiency in renewable energy usage energy storage, and improved user interaction using IoT-based technologies. This research investigates the application of machine learning algorithms to predict energy consumption patterns, electricity prices, and optimal energy storage management strategies. The main idea of the proposed mathematical model in this paper is to minimize the cost of energy procurement under the condition of a balanced energy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex>upply/demand based on the optimization problem. This model is simulated with several scenarios using the linear programming tool of MATLAB software and confirms high cost savings and system efficiency. This study also investigates how EVs and BESS in the grid exert better optimization of smart energy systems.