AI-Driven Micro Solar Power Grid Systems for Remote Communities: Enhancing Renewable Energy Efficiency and Reducing Carbon Emissions
Keywords:
Artificial Intelligence, Renewable Energy, Micro Grid Systems, Solar Power, Carbon Emissions, Energy Efficiency, Machine LearningAbstract
This study, therefore, greatly explores how self-generated AI-powered autonomic micro solar power grid systems in disparate areas improve fresh energy generation and minimize carbon emissions. Expanding upon prior studies, it assesses the application of sophisticated AI methodologies for Predictive Maintenance, Demand Forecasting, and Adaptive Energy Management in fifteen disparate regions using a two-year multiple-baseline design. The performance was impressive, and an increase in energy efficiency was recorded by up to 278%, a reduction in carbon emissions by 213%, and an increase in energy accessibility by 203%. In reaching the visibility of likely system faults and implementing corrective action, here are the rates of success in the achieved predictive maintenance: 89 percent. The rate of success in demand forecasting through machine learning is 92 percent. Another indication that bolstered the economic analyses was the actualized proven efficiencies that identified a 62 percent improved energy consumption efficiency to stimulate local economic activity by 34 percent. According to literature by prior scholars such as the current study underlines the parts played by the populations in rural areas and technological advancements in realizing effective energy solutions. The study affirms the feasibility of using AI in implementing micro solar grids to ease energy poverty and improve the ecological management of remote areas.
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