deep learning based optimal energy management in the presence of renewable energy

Traditional energy management focuses on ensuring a reliable and sustainable energy supply through meticulous planning, coordination, and optimization of resources. however, integrating renewable energy sources like solar, wind, and hydropower introduces a new layer of complexity. these sources, while environmentally friendly, are inherently intermittent and variable in their production, posing unique challenges for energy management. effective energy management in the presence of renewable energy requires strategies to balance supply and demand, optimize energy use, and ensure grid stability. this study introduces a new model designed to significantly improve the accuracy of estimating both energy production and demand. this enhanced level of precision plays a decisive role in the decision-making process for energy management. this innovative model employs a fuzzy neural network trained on historical energy production data, integrating weather information through fuzzy functions to improve precision in estimating energy production for future intervals. the objective functions prioritize renewable energy use to minimize overall system costs. the simulations evaluated the total system cost under various conditions. the results showed that more accurate estimation and maximized utilization of renewable energy sources led to a significant reduction in the cost per kilowatt-hour. in essence, this study offers a promising approach to managing energy systems that heavily rely on renewable sources. by improving estimation accuracy and prioritizing renewable energy use, the model paves the way for a more reliable, sustainable, and cost-effective energy future.