Enhancing Renewable Energy Grids with Predictive Analytics and IoT Integration
Abstract
The global push towards renewable energy sources necessitates efficient grid management to optimize resource distribution. This paper introduces an innovative approach that combines predictive analytics and Internet of Things (IoT) integration to enhance the efficiency of renewable energy grids. Our model leverages machine learning algorithms to predict energy consumption patterns and optimize load balancing, while IoT-enabled sensors provide real-time monitoring of energy distribution. Field experiments conducted on solar and wind energy farms indicate a 20% improvement in energy utilization and a reduction in wastage. This research outlines a scalable solution for sustainable energy management.
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