AI in Smart Energy Forecasting and Optimization
Keywords:
Artificial Intelligence (AI), Energy forecasting, Energy optimization, Low-carbon future, Machine learning, Predictive maintenance, Renewable energy, Smart grid, Sustainable energyAbstract
AI integration with renewable energy systems has started to attend to rising public demands for fulfillment, energy generation, distribution, and optimization. In this way, from variable and intermittent renewable sources such as solar and wind to smart grid management, artificial intelligence technologies are high-tech, robust elements in favor of the enhancement of energy generation and care of predictive maintenance. This statement accounts for AI in optimizing smart grid usage, energy storage management, and forecasting renewables using dynamic weather data. Machine learning-based algorithms/data-driven models learn some better ways to forecast energy production in those fluctuating sources and allow smooth field learning. Intelligent maintenance provides constant supervision to anticipate the failure that might distract the operations. This, in turn, improves the performance and longevity of renewable infrastructures, thereby actually reducing costs while increasing the reliability of the system. Other challenges facing AI applications on renewable energy systems include the availability of data, computational complexity, and integration with other infrastructures. This paper shared some contemporary insights regarding their case studies and emerging technologies, as well as the contribution of artificial intelligence to the sustainable energy drive and the eventual march toward a low-carbon future. AI is fundamentally positioned to enable the sustainable energy transition, adding efficiency and resilience to renewable energy.
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