Integrating IoT and AI For Predictive Maintenance in Smart Power Grid Systems to Minimize Energy Loss and Carbon Footprint
Keywords:
IoT in smart power grids, AI in predictive maintenance, Energy loss reduction, Carbon footprint minimization, Smart power grid systems, Predictive analytics in energyAbstract
Energy systems around the world are currently experiencing several crucial emerging issues such as inadequate energy distribution, deteriorating infrastructure, and increased emissions of carbon. Standard utility systems which are incapable of flexibility in their power distribution as required by increasingly dynamic energy consumption patterns have led to the development of smart power grid systems. Many of these grids use innovative features like the IoT (Internet of Things) and AI (Artificial Intelligence) to further performance, effectiveness, and effectiveness. This article focuses on the application of IoT and AI for implementing the method known as Predictive Maintenance which is concerned with the anticipation of when a piece of equipment is likely to fail. Critical application of predictive maintenance is in the arena of the grid system where it even reduces energy loss and improves the efficiency of the power grid in minimizing the carbon footprint since energy consumption is applied efficiently. This way using big data and the IoT, along with machine learning power grids can learn from live data from the IoT to identify problems before they happen and make efficient changes to avoid costly power outages and wasteful energy use. Such a preventive approach helps to save energy and also is beneficial for the future sustainability of the environment. Using these technologies, the study shows how IoT and AI can optimize energy systems and make energy-efficient, cost-effective, and sustainable for the future consumer interest in a green energy world.IoT in smart power grids
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