Design and Development of Smart Grid Systems for Sustainable Energy Management

Abstract

The researchers outline an organized procedure to create smart grid systems for energy sustainability through machine learning-based load forecasting techniques. The proposed method adopts Support Vector Regression (SVR) for brief-term power need forecasting which optimizes renewable supply management alongside customer usage needs. The implementation utilizes PCA as its feature selection method to achieve better model accuracy and lower computational demands through parameter extraction of temperature and historical load and time of use data. MATLAB/Simulink acts as the platform for executing the implementation through which smart grid performance evaluation alongside model validation and simulation take place. Reusable energy resources achieve better prediction accuracy and response times due to the findings from these studies and they play a critical role in dynamic energy distribution and sustainability. The method enables wise decision support during power grid operations while helping to cut carbon emissions and maximize next-generation renewable energy implementation in power systems.