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This study explores the use of advanced AI models, particularly neural networks, for energy generation forecasting and design optimization in Hybrid Renewable Energy Systems (HRES). Utilizing frameworks like TensorFlow and PyTorch, the research demonstrates that increasing renewable energy share can significantly reduce CO2 emissions, with TensorFlow models showing superior forecasting accuracy. The integration of precise forecasting with location-specific designs enhances the management and sustainability

Author

bhavyasreepandeti

Language

EN