Renewable Energy Optimization Using DataDriven Models: A Comparative Study of Solar and Wind Power Systems
Keywords:
Renewable energy, solar power, wind energyAbstract
The rapid transition toward renewable energy systems requires efficient optimization strategies to improve power generation reliability and grid stability. This study examines the application of data-driven models for optimizing solar and wind energy output. Using multi-year operational datasets, machine learning techniques were applied to predict energy generation and optimize system performance. The findings indicate that data-driven models significantly enhance prediction accuracy and operational efficiency for both solar and wind energy systems. The study highlights the growing importance of artificial intelligence in sustainable energy research and infrastructure planning.
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