Designing Smart Grids with Integrated Renewable Energy Systems

Abstract

The design and optimization of smart grids integrated with renewable energy systems require robust analytical frameworks to address the challenges of data variability, resource allocation, and efficient energy management. This research presents an innovative approach that leverages advanced machine learning techniques, specifically Gradient Boosting Machines (GBMs), combined with MATLAB's machine learning toolbox for enhanced pre-processing, classification, and decision-making. Data from diverse renewable energy sources, including solar, wind, and hydropower, were pre-processed using feature scaling, outlier detection, and dimensionality reduction to ensure high-quality inputs for model training. The GBM algorithm was employed to classify energy consumption patterns and predict grid stability under various operational scenarios. Experimental results demonstrate the proposed methodology's ability to achieve high classification accuracy, improve grid efficiency, and reduce dependency on conventional energy sources. This approach highlights the potential of integrating ML-driven solutions into the design of smart grids, paving the way for sustainable and intelligent energy systems.